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D><yw*tbIpUe?| zgWJ@6N(OuElnlF`n#muct3W!^6N=awPfgyKnZ#%^*)~Im(QvY3Rs@fw4wr(OvazML zfuX_VfFiNUd$PnPFUwK@D*BLNI@v3We{w>mEGJMF0wyVSYECXLQbBTE zVS(u6{#*sBm?Am(e;nuJy!=>}Tt)_l$q#Zp$#oK+aV;#QC^c&H)}kO$R&zr`Gt0?} zohw+4Oe`&pC!gySXE!u9va~QYnS8y&6|el8&TAO*`+6kc`awymZZ`Ym4c##W)X7i& g)6<2a2Iv+6s9Ru(@XzEA9Xi+)!Yth!-R~d+0OQ^ASpWb4 diff --git a/dev/.doctrees/nbsphinx/explanations/hamiltonians.ipynb b/dev/.doctrees/nbsphinx/explanations/hamiltonians.ipynb index fdab3b0e5..a64798d6c 100644 --- a/dev/.doctrees/nbsphinx/explanations/hamiltonians.ipynb +++ b/dev/.doctrees/nbsphinx/explanations/hamiltonians.ipynb @@ -33,10 +33,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:02.728608Z", - "iopub.status.busy": "2024-10-30T01:18:02.728416Z", - "iopub.status.idle": "2024-10-30T01:18:03.436749Z", - "shell.execute_reply": "2024-10-30T01:18:03.436173Z" + "iopub.execute_input": "2024-10-30T02:09:49.027067Z", + "iopub.status.busy": "2024-10-30T02:09:49.026879Z", + "iopub.status.idle": "2024-10-30T02:09:49.763447Z", + "shell.execute_reply": "2024-10-30T02:09:49.762853Z" } }, "outputs": [], @@ -68,10 +68,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:03.439342Z", - "iopub.status.busy": "2024-10-30T01:18:03.438783Z", - "iopub.status.idle": "2024-10-30T01:18:03.441963Z", - "shell.execute_reply": "2024-10-30T01:18:03.441460Z" + "iopub.execute_input": "2024-10-30T02:09:49.765836Z", + "iopub.status.busy": "2024-10-30T02:09:49.765405Z", + "iopub.status.idle": "2024-10-30T02:09:49.768532Z", + "shell.execute_reply": "2024-10-30T02:09:49.768045Z" } }, "outputs": [], @@ -99,10 +99,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:03.443901Z", - "iopub.status.busy": "2024-10-30T01:18:03.443455Z", - "iopub.status.idle": "2024-10-30T01:18:03.446765Z", - "shell.execute_reply": "2024-10-30T01:18:03.446180Z" + "iopub.execute_input": "2024-10-30T02:09:49.770582Z", + "iopub.status.busy": "2024-10-30T02:09:49.770211Z", + "iopub.status.idle": "2024-10-30T02:09:49.773313Z", + "shell.execute_reply": "2024-10-30T02:09:49.772854Z" } }, "outputs": [], @@ -127,10 +127,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:03.449121Z", - "iopub.status.busy": "2024-10-30T01:18:03.448501Z", - "iopub.status.idle": "2024-10-30T01:18:03.453127Z", - "shell.execute_reply": "2024-10-30T01:18:03.452566Z" + "iopub.execute_input": "2024-10-30T02:09:49.775342Z", + "iopub.status.busy": "2024-10-30T02:09:49.774996Z", + "iopub.status.idle": "2024-10-30T02:09:49.779913Z", + "shell.execute_reply": "2024-10-30T02:09:49.779377Z" } }, "outputs": [], @@ -152,17 +152,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:03.456089Z", - "iopub.status.busy": "2024-10-30T01:18:03.455300Z", - "iopub.status.idle": "2024-10-30T01:18:03.483908Z", - "shell.execute_reply": "2024-10-30T01:18:03.483194Z" + "iopub.execute_input": "2024-10-30T02:09:49.782880Z", + "iopub.status.busy": "2024-10-30T02:09:49.782061Z", + "iopub.status.idle": "2024-10-30T02:09:49.811144Z", + "shell.execute_reply": "2024-10-30T02:09:49.810476Z" } }, "outputs": [ { "data": { "text/plain": [ - "np.float64(-99.55717072551545)" + "np.float64(-99.55717072551536)" ] }, "execution_count": 5, @@ -191,10 +191,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:03.518338Z", - "iopub.status.busy": "2024-10-30T01:18:03.517862Z", - "iopub.status.idle": "2024-10-30T01:18:04.161797Z", - "shell.execute_reply": "2024-10-30T01:18:04.161210Z" + "iopub.execute_input": "2024-10-30T02:09:49.843813Z", + "iopub.status.busy": "2024-10-30T02:09:49.843363Z", + "iopub.status.idle": "2024-10-30T02:09:50.493704Z", + "shell.execute_reply": "2024-10-30T02:09:50.493075Z" } }, "outputs": [ @@ -202,7 +202,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_4222/2190712273.py:2: UserWarning: Trace of LinearOperator not available, it will be estimated. Provide `traceA` to ensure performance.\n", + "/tmp/ipykernel_4234/2190712273.py:2: UserWarning: Trace of LinearOperator not available, it will be estimated. Provide `traceA` to ensure performance.\n", " evolved_vec = scipy.sparse.linalg.expm_multiply(-1j * time * linop, vec)\n" ] } @@ -224,10 +224,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:04.165049Z", - "iopub.status.busy": "2024-10-30T01:18:04.164211Z", - "iopub.status.idle": "2024-10-30T01:18:04.752315Z", - "shell.execute_reply": "2024-10-30T01:18:04.751757Z" + "iopub.execute_input": "2024-10-30T02:09:50.497160Z", + "iopub.status.busy": "2024-10-30T02:09:50.496198Z", + "iopub.status.idle": "2024-10-30T02:09:51.112728Z", + "shell.execute_reply": "2024-10-30T02:09:51.112100Z" } }, "outputs": [], diff --git a/dev/.doctrees/nbsphinx/explanations/orbital-rotation.ipynb b/dev/.doctrees/nbsphinx/explanations/orbital-rotation.ipynb index 068c624ac..53155bfc3 100644 --- a/dev/.doctrees/nbsphinx/explanations/orbital-rotation.ipynb +++ b/dev/.doctrees/nbsphinx/explanations/orbital-rotation.ipynb @@ -62,10 +62,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:07.610992Z", - "iopub.status.busy": "2024-10-30T01:18:07.610806Z", - "iopub.status.idle": "2024-10-30T01:18:08.313302Z", - "shell.execute_reply": "2024-10-30T01:18:08.312639Z" + "iopub.execute_input": "2024-10-30T02:09:54.371994Z", + "iopub.status.busy": "2024-10-30T02:09:54.371552Z", + "iopub.status.idle": "2024-10-30T02:09:55.112686Z", + "shell.execute_reply": "2024-10-30T02:09:55.112160Z" } }, "outputs": [], diff --git a/dev/.doctrees/nbsphinx/explanations/qiskit-gate-decompositions.ipynb b/dev/.doctrees/nbsphinx/explanations/qiskit-gate-decompositions.ipynb index 37a68f9f6..cbe4ca86c 100644 --- a/dev/.doctrees/nbsphinx/explanations/qiskit-gate-decompositions.ipynb +++ b/dev/.doctrees/nbsphinx/explanations/qiskit-gate-decompositions.ipynb @@ -16,10 +16,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:09.747117Z", - "iopub.status.busy": "2024-10-30T01:18:09.746661Z", - "iopub.status.idle": "2024-10-30T01:18:12.572783Z", - "shell.execute_reply": "2024-10-30T01:18:12.572145Z" + "iopub.execute_input": "2024-10-30T02:09:56.540941Z", + "iopub.status.busy": "2024-10-30T02:09:56.540747Z", + "iopub.status.idle": "2024-10-30T02:09:58.179701Z", + "shell.execute_reply": "2024-10-30T02:09:58.179102Z" } }, "outputs": [ @@ -81,10 +81,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:12.575149Z", - "iopub.status.busy": "2024-10-30T01:18:12.574683Z", - "iopub.status.idle": "2024-10-30T01:18:12.763089Z", - "shell.execute_reply": "2024-10-30T01:18:12.762506Z" + "iopub.execute_input": "2024-10-30T02:09:58.182043Z", + "iopub.status.busy": "2024-10-30T02:09:58.181548Z", + "iopub.status.idle": "2024-10-30T02:09:58.388591Z", + "shell.execute_reply": "2024-10-30T02:09:58.387988Z" } }, "outputs": [ @@ -119,10 +119,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:12.765303Z", - "iopub.status.busy": "2024-10-30T01:18:12.764966Z", - "iopub.status.idle": "2024-10-30T01:18:12.872452Z", - "shell.execute_reply": "2024-10-30T01:18:12.871846Z" + "iopub.execute_input": "2024-10-30T02:09:58.390983Z", + "iopub.status.busy": "2024-10-30T02:09:58.390694Z", + "iopub.status.idle": "2024-10-30T02:09:58.500594Z", + "shell.execute_reply": "2024-10-30T02:09:58.500004Z" } }, "outputs": [ @@ -156,10 +156,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:12.874397Z", - "iopub.status.busy": "2024-10-30T01:18:12.874061Z", - "iopub.status.idle": "2024-10-30T01:18:12.982494Z", - "shell.execute_reply": "2024-10-30T01:18:12.981922Z" + "iopub.execute_input": "2024-10-30T02:09:58.502908Z", + "iopub.status.busy": "2024-10-30T02:09:58.502544Z", + "iopub.status.idle": "2024-10-30T02:09:58.613368Z", + "shell.execute_reply": "2024-10-30T02:09:58.612753Z" } }, "outputs": [ @@ -196,10 +196,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:12.984356Z", - "iopub.status.busy": "2024-10-30T01:18:12.984142Z", - "iopub.status.idle": "2024-10-30T01:18:13.165649Z", - "shell.execute_reply": "2024-10-30T01:18:13.165155Z" + "iopub.execute_input": "2024-10-30T02:09:58.615746Z", + "iopub.status.busy": "2024-10-30T02:09:58.615356Z", + "iopub.status.idle": "2024-10-30T02:09:58.800638Z", + "shell.execute_reply": "2024-10-30T02:09:58.800009Z" } }, "outputs": [ @@ -250,10 +250,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:13.167631Z", - "iopub.status.busy": "2024-10-30T01:18:13.167340Z", - "iopub.status.idle": "2024-10-30T01:18:13.384578Z", - "shell.execute_reply": "2024-10-30T01:18:13.384059Z" + "iopub.execute_input": "2024-10-30T02:09:58.803009Z", + "iopub.status.busy": "2024-10-30T02:09:58.802627Z", + "iopub.status.idle": "2024-10-30T02:09:59.025496Z", + "shell.execute_reply": "2024-10-30T02:09:59.024902Z" } }, "outputs": [ @@ -292,10 +292,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:13.386502Z", - "iopub.status.busy": "2024-10-30T01:18:13.386129Z", - "iopub.status.idle": "2024-10-30T01:18:13.518827Z", - "shell.execute_reply": "2024-10-30T01:18:13.518261Z" + "iopub.execute_input": "2024-10-30T02:09:59.027842Z", + "iopub.status.busy": "2024-10-30T02:09:59.027426Z", + "iopub.status.idle": "2024-10-30T02:09:59.164215Z", + "shell.execute_reply": "2024-10-30T02:09:59.163691Z" } }, "outputs": [ @@ -334,10 +334,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:13.520818Z", - "iopub.status.busy": "2024-10-30T01:18:13.520487Z", - "iopub.status.idle": "2024-10-30T01:18:14.031045Z", - "shell.execute_reply": "2024-10-30T01:18:14.030435Z" + "iopub.execute_input": "2024-10-30T02:09:59.166452Z", + "iopub.status.busy": "2024-10-30T02:09:59.165894Z", + "iopub.status.idle": "2024-10-30T02:09:59.695006Z", + "shell.execute_reply": "2024-10-30T02:09:59.694428Z" } }, "outputs": [ @@ -378,10 +378,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:14.033274Z", - "iopub.status.busy": "2024-10-30T01:18:14.032831Z", - "iopub.status.idle": "2024-10-30T01:18:14.214030Z", - "shell.execute_reply": "2024-10-30T01:18:14.213518Z" + "iopub.execute_input": "2024-10-30T02:09:59.697356Z", + "iopub.status.busy": "2024-10-30T02:09:59.696979Z", + "iopub.status.idle": "2024-10-30T02:09:59.886497Z", + "shell.execute_reply": "2024-10-30T02:09:59.885807Z" } }, "outputs": [ @@ -430,10 +430,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:14.216075Z", - "iopub.status.busy": "2024-10-30T01:18:14.215684Z", - "iopub.status.idle": "2024-10-30T01:18:14.382744Z", - "shell.execute_reply": "2024-10-30T01:18:14.382266Z" + "iopub.execute_input": "2024-10-30T02:09:59.889396Z", + "iopub.status.busy": "2024-10-30T02:09:59.888809Z", + "iopub.status.idle": "2024-10-30T02:10:00.090115Z", + "shell.execute_reply": "2024-10-30T02:10:00.089589Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:14.384733Z", - "iopub.status.busy": "2024-10-30T01:18:14.384351Z", - "iopub.status.idle": "2024-10-30T01:18:14.513562Z", - "shell.execute_reply": "2024-10-30T01:18:14.513039Z" + "iopub.execute_input": "2024-10-30T02:10:00.092257Z", + "iopub.status.busy": "2024-10-30T02:10:00.091864Z", + "iopub.status.idle": "2024-10-30T02:10:00.223234Z", + "shell.execute_reply": "2024-10-30T02:10:00.222527Z" } }, "outputs": [ @@ -513,10 +513,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:14.515595Z", - "iopub.status.busy": "2024-10-30T01:18:14.515232Z", - "iopub.status.idle": "2024-10-30T01:18:14.692478Z", - "shell.execute_reply": "2024-10-30T01:18:14.691934Z" + "iopub.execute_input": "2024-10-30T02:10:00.225524Z", + "iopub.status.busy": "2024-10-30T02:10:00.225171Z", + "iopub.status.idle": "2024-10-30T02:10:00.404126Z", + "shell.execute_reply": "2024-10-30T02:10:00.403500Z" } }, "outputs": [ @@ -553,10 +553,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:14.694463Z", - "iopub.status.busy": "2024-10-30T01:18:14.694089Z", - "iopub.status.idle": "2024-10-30T01:18:14.851809Z", - "shell.execute_reply": "2024-10-30T01:18:14.851280Z" + "iopub.execute_input": "2024-10-30T02:10:00.406362Z", + "iopub.status.busy": "2024-10-30T02:10:00.405851Z", + "iopub.status.idle": "2024-10-30T02:10:00.565779Z", + "shell.execute_reply": "2024-10-30T02:10:00.565154Z" } }, "outputs": [ @@ -593,10 +593,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:14.853676Z", - "iopub.status.busy": "2024-10-30T01:18:14.853481Z", - "iopub.status.idle": "2024-10-30T01:18:14.983082Z", - "shell.execute_reply": "2024-10-30T01:18:14.982518Z" + "iopub.execute_input": "2024-10-30T02:10:00.568093Z", + "iopub.status.busy": "2024-10-30T02:10:00.567623Z", + "iopub.status.idle": "2024-10-30T02:10:00.699470Z", + "shell.execute_reply": "2024-10-30T02:10:00.698933Z" } }, "outputs": [ @@ -630,10 +630,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:14.985145Z", - "iopub.status.busy": "2024-10-30T01:18:14.984776Z", - "iopub.status.idle": "2024-10-30T01:18:15.142916Z", - "shell.execute_reply": "2024-10-30T01:18:15.142318Z" + "iopub.execute_input": "2024-10-30T02:10:00.701705Z", + "iopub.status.busy": "2024-10-30T02:10:00.701337Z", + "iopub.status.idle": "2024-10-30T02:10:00.860858Z", + "shell.execute_reply": "2024-10-30T02:10:00.860218Z" } }, "outputs": [ @@ -677,10 +677,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:15.145121Z", - "iopub.status.busy": "2024-10-30T01:18:15.144676Z", - "iopub.status.idle": "2024-10-30T01:18:15.322744Z", - "shell.execute_reply": "2024-10-30T01:18:15.322201Z" + "iopub.execute_input": "2024-10-30T02:10:00.863094Z", + "iopub.status.busy": "2024-10-30T02:10:00.862748Z", + "iopub.status.idle": "2024-10-30T02:10:01.058547Z", + "shell.execute_reply": "2024-10-30T02:10:01.058004Z" } }, "outputs": [ @@ -736,16 +736,16 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:15.325193Z", - "iopub.status.busy": "2024-10-30T01:18:15.324673Z", - "iopub.status.idle": "2024-10-30T01:18:15.789795Z", - "shell.execute_reply": "2024-10-30T01:18:15.789259Z" + "iopub.execute_input": "2024-10-30T02:10:01.060929Z", + "iopub.status.busy": "2024-10-30T02:10:01.060430Z", + "iopub.status.idle": "2024-10-30T02:10:01.510763Z", + "shell.execute_reply": "2024-10-30T02:10:01.510137Z" } }, "outputs": [ { "data": { - 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", 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", 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    " ] @@ -776,10 +776,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:15.791942Z", - "iopub.status.busy": "2024-10-30T01:18:15.791589Z", - "iopub.status.idle": "2024-10-30T01:18:16.048206Z", - "shell.execute_reply": "2024-10-30T01:18:16.047702Z" + "iopub.execute_input": "2024-10-30T02:10:01.512769Z", + "iopub.status.busy": "2024-10-30T02:10:01.512568Z", + "iopub.status.idle": "2024-10-30T02:10:01.773917Z", + "shell.execute_reply": "2024-10-30T02:10:01.773307Z" } }, "outputs": [ diff --git a/dev/.doctrees/nbsphinx/explanations/state-vectors-and-gates.ipynb b/dev/.doctrees/nbsphinx/explanations/state-vectors-and-gates.ipynb index 4485797a4..4c54e759f 100644 --- a/dev/.doctrees/nbsphinx/explanations/state-vectors-and-gates.ipynb +++ b/dev/.doctrees/nbsphinx/explanations/state-vectors-and-gates.ipynb @@ -26,10 +26,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:18.353825Z", - "iopub.status.busy": "2024-10-30T01:18:18.353580Z", - "iopub.status.idle": "2024-10-30T01:18:19.070555Z", - "shell.execute_reply": "2024-10-30T01:18:19.070020Z" + "iopub.execute_input": "2024-10-30T02:10:04.418025Z", + "iopub.status.busy": "2024-10-30T02:10:04.417480Z", + "iopub.status.idle": "2024-10-30T02:10:05.125267Z", + "shell.execute_reply": "2024-10-30T02:10:05.124634Z" } }, "outputs": [ @@ -74,10 +74,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:19.072948Z", - "iopub.status.busy": "2024-10-30T01:18:19.072497Z", - "iopub.status.idle": "2024-10-30T01:18:19.079196Z", - "shell.execute_reply": "2024-10-30T01:18:19.078623Z" + "iopub.execute_input": "2024-10-30T02:10:05.127739Z", + "iopub.status.busy": "2024-10-30T02:10:05.127148Z", + "iopub.status.idle": "2024-10-30T02:10:05.134277Z", + "shell.execute_reply": "2024-10-30T02:10:05.133694Z" } }, "outputs": [ @@ -120,10 +120,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:19.081471Z", - "iopub.status.busy": "2024-10-30T01:18:19.080918Z", - "iopub.status.idle": "2024-10-30T01:18:19.085462Z", - "shell.execute_reply": "2024-10-30T01:18:19.084996Z" + "iopub.execute_input": "2024-10-30T02:10:05.136245Z", + "iopub.status.busy": "2024-10-30T02:10:05.135916Z", + "iopub.status.idle": "2024-10-30T02:10:05.140337Z", + "shell.execute_reply": "2024-10-30T02:10:05.139769Z" } }, "outputs": [ @@ -157,10 +157,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:19.087595Z", - "iopub.status.busy": "2024-10-30T01:18:19.087118Z", - "iopub.status.idle": "2024-10-30T01:18:19.091217Z", - "shell.execute_reply": "2024-10-30T01:18:19.090667Z" + "iopub.execute_input": "2024-10-30T02:10:05.142414Z", + "iopub.status.busy": "2024-10-30T02:10:05.141917Z", + "iopub.status.idle": "2024-10-30T02:10:05.146148Z", + "shell.execute_reply": "2024-10-30T02:10:05.145687Z" } }, "outputs": [ @@ -199,10 +199,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:19.093127Z", - "iopub.status.busy": "2024-10-30T01:18:19.092764Z", - "iopub.status.idle": "2024-10-30T01:18:19.098476Z", - "shell.execute_reply": "2024-10-30T01:18:19.097893Z" + "iopub.execute_input": "2024-10-30T02:10:05.148356Z", + "iopub.status.busy": "2024-10-30T02:10:05.147900Z", + "iopub.status.idle": "2024-10-30T02:10:05.153848Z", + "shell.execute_reply": "2024-10-30T02:10:05.153312Z" } }, "outputs": [ @@ -245,10 +245,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:19.100480Z", - "iopub.status.busy": "2024-10-30T01:18:19.100097Z", - "iopub.status.idle": "2024-10-30T01:18:19.105467Z", - "shell.execute_reply": "2024-10-30T01:18:19.104906Z" + "iopub.execute_input": "2024-10-30T02:10:05.155963Z", + "iopub.status.busy": "2024-10-30T02:10:05.155611Z", + "iopub.status.idle": "2024-10-30T02:10:05.161396Z", + "shell.execute_reply": "2024-10-30T02:10:05.160798Z" } }, "outputs": [ @@ -293,10 +293,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:19.107433Z", - "iopub.status.busy": "2024-10-30T01:18:19.107085Z", - "iopub.status.idle": "2024-10-30T01:18:19.112091Z", - "shell.execute_reply": "2024-10-30T01:18:19.111524Z" + "iopub.execute_input": "2024-10-30T02:10:05.163370Z", + "iopub.status.busy": "2024-10-30T02:10:05.163050Z", + "iopub.status.idle": "2024-10-30T02:10:05.168129Z", + "shell.execute_reply": "2024-10-30T02:10:05.167557Z" } }, "outputs": [ diff --git a/dev/.doctrees/nbsphinx/explanations_qiskit-gate-decompositions_34_0.png b/dev/.doctrees/nbsphinx/explanations_qiskit-gate-decompositions_34_0.png index d214a4097d434aebe4ffd6e5c812f80eb4c79f73..5555b645a61cf94753627c818a9e19a1e5f7ddd1 100644 GIT binary patch literal 55030 zcmd?Rby!vHx;Huz1Q7&jlu{57>FyE)q(!B>ySowT5|BLy5DR{Y-Ghj$?m$i0^mq6!cQQZ)pE_~Z^U_=$WZ 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    <~IL=#22>-zw7 zb>NTKc^UyV@h4$_!=)qn*ubff2hMfHZTn2>%YeR`xY{+T^GA9cLUEtbZ2>8@O1Oh* zwB2(iC3yG4ce>sNNZyW)Ork0v6RfNCCy+BqU~p$}eS8}`O1G6qB_`e?cLqX@kH0p{ z!=2$g-r`H=jLau2kx(?RqV|C{8e>;&NU#;sMFDbw{y|Pn@d{Dv;Lf4S?5^3V?Pe8< zb*T+j_aZK?<{lxH$~v#za61v7i6@G_F99!+!wg3)UYW4cVMZ{&J!Xp+-m8kjZJt>N zb`;lDAQ?0hql)*z*+g-BMn*;VBsUmR^Q)AMYMsKHPu3IAEYUNy?*m=5?ooMP|uWQF!_XM)AZ&#BNKvg(vdp z(WCmaDS|?N&OgbI-C@7}mjd&@*XmB-M1DNJ5&&D=Rn^t8u&V|lxJFDY07Nr<#8v}C zeM>PvCkA|VtPP`u8S}cvFRT((apdZs$`WM9JYTc_zu+2k!tzBOi^e9O+RuGwGaT3H zH!R3s|26>&_ml0990p>%+){nB8EIxq=K4>=D8Su8wWziF#lSaq=+oT*lW=7_iI14C z%6SuYD#=P?B&)b2ZZvs`2M2bN0`-cT8?2*+^T5nMd9UF^vSyuD;vJo`>`ezbg>MTg zeUbR*<9yPqlcM@#)+2kPPUnnj?8xhV)vRpLO!Xh69t@14jQuc%-+G{ zB_~A@U-|ZhY}Yf_mkX?&Sr=X}F>GkJxvVGeIdw5N@&5VPIQmqW)MOR=y-1uhjxTr0 zEf{VIN@$6xV>FZ&Ct8rw-bJ)&3DKvSTrn2GX6z?<+EC=UrR+|dS1{ayKRsM%a2gY) z5|*36vxF=4`t|FbNA(@trMXf#L$bJWwLCJ|j#}SzfVI!tclUb-;wCC-C0Wb&87nVs z9A2vQP&|_U5c{Q!Goj#9U6h$O-Fj-=PKVbo$Ul$cI`DwR{?VMzxv$GX81!nsyz1U| zsq`f!vpWP}Be3WHVk2Nq%a%jIh~C9zKlV56pS$a`TqHq(GjfI|E8xc;9~`IA zP{*<*uV+01`0T=&2R?OQXmop*`(>Gss^(&o8;|yRZ}w)%piR`$@^t2;N6tQ7X0zUX zYW?f9>6pt3OgW4ZvvYdDW=`vW3U{)0lYOTx6#uGh;j7FI++cTMce!hZ`w?(b4 zE3Iw(?adavC1FPKd#uvW@wQ3x~Iq+7f# z6FpteBWv7sjr<(@Oh1kUv>N!at8B1S(z!8s1U7d^k1i)J)R@no{>@sAw8#WNHdXp8 znKN;qOr~EJ2JoK+E>5FQL3MULDAT*0M}pcESGSoqFSMQaVmR9t5f1k+Qp-6(Nz(Js zU#ziNZe;rk&kn}C*aIgI-?r}=%JJ$BFn-alCsQDCPunQKUKTi3x=rqAgm-_?GB4~B z*H6BS-z;?HkCDL9wBBjq9ODAt>mV8D6sA!N9cF{4%>N<^!n^LD5d~Al_tb(AbOBt! z^dHnX|M9_<5*A8u1&(v8V5mr1`mm{|Vy;plcfc`;suCFWg{P^ng4P;BZ*O(7BD%0b zZi(~`*>Nepc@{!>K3ysiA4zw>mfp2b-? z@a0sV;f)j%xw|`=!}M6h61=STR$h;s-PHV}wDY}~YM@t7=8kika(B}fHyLt0s?vof z(qg6CCfTHenWDk|`cqdb%~Lr-3q-^7%396dOGF9XD7_rdNzbI|puL{KoyIylDx8_d z{<-%?ij(z19Y;@_*M3(rt8Pzr&SGl!V%JJGdKI^p-+g=ynM2V4gFCdXslD)kEqzM= zrTPzqk!W%M>)XUQ?jJD!+sMhWgYmC(D4?FrjH=3e^mD#REk$H_WEDj{0FoE diff --git a/dev/_modules/ffsim/qiskit/gates/givens_ansatz.html b/dev/_modules/ffsim/qiskit/gates/givens_ansatz.html index 7a260b392..5d51a8f75 100644 --- a/dev/_modules/ffsim/qiskit/gates/givens_ansatz.html +++ b/dev/_modules/ffsim/qiskit/gates/givens_ansatz.html @@ -278,7 +278,6 @@

    Source code for ffsim.qiskit.gates.givens_ansatz

    from __future__ import annotations -import itertools import math from collections.abc import Iterator, Sequence @@ -291,109 +290,8 @@

    Source code for ffsim.qiskit.gates.givens_ansatz

    Qubit, ) from qiskit.circuit.library import PhaseGate, XXPlusYYGate -from typing_extensions import deprecated -from ffsim.variational import GivensAnsatzOp, GivensAnsatzOperator - - -
    -[docs] -@deprecated("GivensAnsatzOperatorJW is deprecated. Use GivensAnsatzOpJW instead.") -class GivensAnsatzOperatorJW(Gate): - """Givens rotation ansatz operator under the Jordan-Wigner transformation. - - .. warning:: - This class is deprecated. Use :class:`ffsim.qiskit.GivensAnsatzOpJW` instead. - - See :class:`ffsim.GivensAnsatzOperator` for a description of this gate's unitary. - """ - -
    -[docs] - def __init__( - self, givens_ansatz_operator: GivensAnsatzOperator, *, label: str | None = None - ): - """Create a new Givens ansatz operator gate. - - Args: - givens_ansatz_operator: The Givens rotation ansatz operator. - label: The label of the gate. - """ - self.givens_ansatz_operator = givens_ansatz_operator - super().__init__( - "givens_ansatz_jw", 2 * givens_ansatz_operator.norb, [], label=label - )
    - - - def _define(self): - """Gate decomposition.""" - qubits = QuantumRegister(self.num_qubits) - circuit = QuantumCircuit(qubits, name=self.name) - norb = len(qubits) // 2 - alpha_qubits = qubits[:norb] - beta_qubits = qubits[norb:] - for instruction in _givens_ansatz_operator_jw( - alpha_qubits, self.givens_ansatz_operator - ): - circuit.append(instruction) - for instruction in _givens_ansatz_operator_jw( - beta_qubits, self.givens_ansatz_operator - ): - circuit.append(instruction) - self.definition = circuit
    - - - -
    -[docs] -@deprecated( - "GivensAnsatzOperatorSpinlessJW is deprecated. " - "Use GivensAnsatzOpSpinlessJW instead." -) -class GivensAnsatzOperatorSpinlessJW(Gate): - """Givens rotation ansatz operator under the Jordan-Wigner transformation, spinless. - - Like :class:`GivensAnsatzOperatorJW` but only acts on a single spin species. - """ - -
    -[docs] - def __init__( - self, givens_ansatz_operator: GivensAnsatzOperator, *, label: str | None = None - ): - """Create a new Givens ansatz operator gate. - - Args: - givens_ansatz_operator: The Givens rotation ansatz operator. - label: The label of the gate. - """ - self.givens_ansatz_operator = givens_ansatz_operator - super().__init__( - "givens_ansatz_jw", givens_ansatz_operator.norb, [], label=label - )
    - - - def _define(self): - """Gate decomposition.""" - qubits = QuantumRegister(self.num_qubits) - self.definition = QuantumCircuit.from_instructions( - _givens_ansatz_operator_jw(qubits, self.givens_ansatz_operator), - qubits=qubits, - name=self.name, - )
    - - - -def _givens_ansatz_operator_jw( - qubits: Sequence[Qubit], givens_ansatz_operator: GivensAnsatzOperator -) -> Iterator[CircuitInstruction]: - for (i, j), theta in zip( - itertools.cycle(givens_ansatz_operator.interaction_pairs), - givens_ansatz_operator.thetas, - ): - yield CircuitInstruction( - XXPlusYYGate(2 * theta, -0.5 * math.pi), (qubits[i], qubits[j]) - ) +from ffsim.variational import GivensAnsatzOp
    diff --git a/dev/_modules/ffsim/qiskit/gates/ucj_operator.html b/dev/_modules/ffsim/qiskit/gates/ucj_operator.html deleted file mode 100644 index 79a38f70c..000000000 --- a/dev/_modules/ffsim/qiskit/gates/ucj_operator.html +++ /dev/null @@ -1,395 +0,0 @@ - - - - - - - - ffsim.qiskit.gates.ucj_operator - ffsim 0.0.48.dev0 - - - - - - - - - - - - - - - - - Contents - - - - - - Menu - - - - - - - - - Expand - - - - - - Light mode - - - - - - - - - - - - - - Dark mode - - - - - - - Auto light/dark, in light mode - - - - - - - - - - - - - - - Auto light/dark, in dark mode - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Skip to content - - - -
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    Source code for ffsim.qiskit.gates.ucj_operator

    -# (C) Copyright IBM 2024.
    -#
    -# This code is licensed under the Apache License, Version 2.0. You may
    -# obtain a copy of this license in the LICENSE.txt file in the root directory
    -# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
    -#
    -# Any modifications or derivative works of this code must retain this
    -# copyright notice, and modified files need to carry a notice indicating
    -# that they have been altered from the originals.
    -
    -"""(Local) unitary cluster Jastrow ansatz gate."""
    -
    -from __future__ import annotations
    -
    -from collections.abc import Iterator, Sequence
    -
    -from qiskit.circuit import (
    -    CircuitInstruction,
    -    Gate,
    -    QuantumCircuit,
    -    QuantumRegister,
    -    Qubit,
    -)
    -from typing_extensions import deprecated
    -
    -from ffsim.qiskit.gates.diag_coulomb import DiagCoulombEvolutionJW
    -from ffsim.qiskit.gates.orbital_rotation import OrbitalRotationJW
    -from ffsim.variational import UCJOperator
    -
    -
    -
    -[docs] -@deprecated("The UCJOperatorJW class is deprecated. Use UCJOpSpinBalancedJW instead.") -class UCJOperatorJW(Gate): - """Unitary cluster Jastrow operator under the Jordan-Wigner transformation. - - .. warning:: - This class is deprecated. Use :class:`ffsim.qiskit.UCJOpSpinBalancedJW` instead. - - See :class:`ffsim.UCJOperator` for a description of this gate's unitary. - - This gate assumes that qubits are ordered such that the first `norb` qubits - correspond to the alpha orbitals and the last `norb` qubits correspond to the - beta orbitals. - """ - -
    -[docs] - def __init__(self, ucj_operator: UCJOperator, *, label: str | None = None): - """Create a new unitary cluster Jastrow (UCJ) gate. - - Args: - ucj_operator: The UCJ operator. - label: The label of the gate. - """ - self.ucj_operator = ucj_operator - super().__init__("ucj_jw", 2 * ucj_operator.norb, [], label=label)
    - - - def _define(self): - """Gate decomposition.""" - qubits = QuantumRegister(self.num_qubits) - self.definition = QuantumCircuit.from_instructions( - _ucj_jw(qubits, self.ucj_operator), qubits=qubits, name=self.name - )
    - - - -def _ucj_jw( - qubits: Sequence[Qubit], ucj_op: UCJOperator -) -> Iterator[CircuitInstruction]: - for mat, mat_alpha_beta, orbital_rotation in zip( - ucj_op.diag_coulomb_mats_alpha_alpha, - ucj_op.diag_coulomb_mats_alpha_beta, - ucj_op.orbital_rotations, - ): - yield CircuitInstruction( - OrbitalRotationJW(ucj_op.norb, orbital_rotation.T.conj()), qubits - ) - yield CircuitInstruction( - DiagCoulombEvolutionJW(ucj_op.norb, (mat, mat_alpha_beta, mat), -1.0), - qubits, - ) - yield CircuitInstruction( - OrbitalRotationJW(ucj_op.norb, orbital_rotation), qubits - ) - if ucj_op.final_orbital_rotation is not None: - yield CircuitInstruction( - OrbitalRotationJW(ucj_op.norb, ucj_op.final_orbital_rotation), qubits - ) -
    -
    -
    -
    - - -
    -
    - - Made with Sphinx and @pradyunsg's - - Furo - -
    -
    - -
    -
    - -
    -
    - -
    -
    - - - - - - \ No newline at end of file diff --git a/dev/_modules/ffsim/qiskit/sim.html b/dev/_modules/ffsim/qiskit/sim.html index 962bd2a0a..b49ddbabb 100644 --- a/dev/_modules/ffsim/qiskit/sim.html +++ b/dev/_modules/ffsim/qiskit/sim.html @@ -287,8 +287,8 @@

    Source code for ffsim.qiskit.sim

     from ffsim.qiskit.gates import (
         DiagCoulombEvolutionJW,
         DiagCoulombEvolutionSpinlessJW,
    -    GivensAnsatzOperatorJW,
    -    GivensAnsatzOperatorSpinlessJW,
    +    GivensAnsatzOpJW,
    +    GivensAnsatzOpSpinlessJW,
         OrbitalRotationJW,
         OrbitalRotationSpinlessJW,
         PrepareHartreeFockJW,
    @@ -296,7 +296,6 @@ 

    Source code for ffsim.qiskit.sim

         PrepareSlaterDeterminantJW,
         PrepareSlaterDeterminantSpinlessJW,
         SimulateTrotterDoubleFactorizedJW,
    -    UCJOperatorJW,
         UCJOpSpinBalancedJW,
         UCJOpSpinlessJW,
         UCJOpSpinUnbalancedJW,
    @@ -405,14 +404,14 @@ 

    Source code for ffsim.qiskit.sim

             )
             return states.StateVector(vec=vec, norb=norb, nelec=nelec)
     
    -    if isinstance(op, GivensAnsatzOperatorSpinlessJW):
    +    if isinstance(op, GivensAnsatzOpSpinlessJW):
             if not consecutive_sorted:
                 raise ValueError(
                     f"Gate of type '{op.__class__.__name__}' must be applied to "
                     "consecutive qubits, in ascending order."
                 )
             vec = protocols.apply_unitary(
    -            vec, op.givens_ansatz_operator, norb=norb, nelec=nelec, copy=False
    +            vec, op.givens_ansatz_op, norb=norb, nelec=nelec, copy=False
             )
             return states.StateVector(vec=vec, norb=norb, nelec=nelec)
     
    @@ -475,14 +474,14 @@ 

    Source code for ffsim.qiskit.sim

             )
             return states.StateVector(vec=vec, norb=norb, nelec=nelec)
     
    -    if isinstance(op, GivensAnsatzOperatorJW):
    +    if isinstance(op, GivensAnsatzOpJW):
             if not consecutive_sorted:
                 raise ValueError(
                     f"Gate of type '{op.__class__.__name__}' must be applied to "
                     "consecutive qubits, in ascending order."
                 )
             vec = protocols.apply_unitary(
    -            vec, op.givens_ansatz_operator, norb=norb, nelec=nelec, copy=False
    +            vec, op.givens_ansatz_op, norb=norb, nelec=nelec, copy=False
             )
             return states.StateVector(vec=vec, norb=norb, nelec=nelec)
     
    @@ -532,17 +531,6 @@ 

    Source code for ffsim.qiskit.sim

             )
             return states.StateVector(vec=vec, norb=norb, nelec=nelec)
     
    -    if isinstance(op, UCJOperatorJW):
    -        if not consecutive_sorted:
    -            raise ValueError(
    -                f"Gate of type '{op.__class__.__name__}' must be applied to "
    -                "consecutive qubits, in ascending order."
    -            )
    -        vec = protocols.apply_unitary(
    -            vec, op.ucj_operator, norb=norb, nelec=nelec, copy=False
    -        )
    -        return states.StateVector(vec=vec, norb=norb, nelec=nelec)
    -
         if isinstance(op, Barrier):
             return state_vector
     
    diff --git a/dev/_modules/ffsim/random/random.html b/dev/_modules/ffsim/random/random.html
    index eca0fc0ad..e90fc15f5 100644
    --- a/dev/_modules/ffsim/random/random.html
    +++ b/dev/_modules/ffsim/random/random.html
    @@ -599,54 +599,6 @@ 

    Source code for ffsim.random.random

     
     
     
    -
    -[docs] -@deprecated( - "The random_ucj_operator function is deprecated. Use " - "random_ucj_operator_closed_shell or random_ucj_operator_open_shell instead." -) -def random_ucj_operator( - norb: int, - *, - n_reps: int = 1, - with_final_orbital_rotation: bool = False, - seed=None, -) -> variational.UCJOperator: - """Sample a random unitary cluster Jastrow (UCJ) operator. - - Args: - norb: The number of orbitals. - n_reps: The number of ansatz repetitions. - with_final_orbital_rotation: Whether to include a final orbital rotation - in the operator. - seed: A seed to initialize the pseudorandom number generator. - Should be a valid input to ``np.random.default_rng``. - - Returns: - The sampled UCJ operator. - """ - rng = np.random.default_rng(seed) - diag_coulomb_mats_alpha_alpha = np.stack( - [random_real_symmetric_matrix(norb, seed=rng) for _ in range(n_reps)] - ) - diag_coulomb_mats_alpha_beta = np.stack( - [random_real_symmetric_matrix(norb, seed=rng) for _ in range(n_reps)] - ) - orbital_rotations = np.stack( - [random_unitary(norb, seed=rng) for _ in range(n_reps)] - ) - final_orbital_rotation = None - if with_final_orbital_rotation: - final_orbital_rotation = random_unitary(norb, seed=rng) - return variational.UCJOperator( - diag_coulomb_mats_alpha_alpha=diag_coulomb_mats_alpha_alpha, - diag_coulomb_mats_alpha_beta=diag_coulomb_mats_alpha_beta, - orbital_rotations=orbital_rotations, - final_orbital_rotation=final_orbital_rotation, - )
    - - -
    [docs] def random_uccsd_restricted( diff --git a/dev/_modules/ffsim/variational/givens.html b/dev/_modules/ffsim/variational/givens.html index a05f95b98..2b9e22768 100644 --- a/dev/_modules/ffsim/variational/givens.html +++ b/dev/_modules/ffsim/variational/givens.html @@ -285,91 +285,12 @@

    Source code for ffsim.variational.givens

     from typing import cast
     
     import numpy as np
    -from scipy.linalg.blas import drot
     from scipy.linalg.lapack import zrot
    -from typing_extensions import deprecated
     
     from ffsim import linalg
     from ffsim.gates import apply_orbital_rotation
     
     
    -
    -[docs] -@dataclass(frozen=True) -@deprecated("GivensAnsatzOperator is deprecated. Use GivensAnsatzOp instead.") -class GivensAnsatzOperator: - """A Givens rotation ansatz operator. - - .. warning:: - This class is deprecated. Use :class:`ffsim.GivensAnsatzOp` instead. - - The Givens rotation ansatz consists of a sequence of `Givens rotations`_. - - Note that this ansatz does not implement any interactions between spin alpha and - spin beta orbitals. - - Attributes: - norb (int): The number of spatial orbitals. - interaction_pairs (list[tuple[int, int]]): The orbital pairs to apply the Givens - rotations to. - thetas (np.ndarray): The angles for the Givens rotations. - - .. _Givens rotations: ffsim.html#ffsim.apply_givens_rotation - """ - - norb: int - interaction_pairs: list[tuple[int, int]] - thetas: np.ndarray - - def _apply_unitary_( - self, vec: np.ndarray, norb: int, nelec: int | tuple[int, int], copy: bool - ) -> np.ndarray: - """Apply the operator to a vector.""" - return apply_orbital_rotation( - vec, self.to_orbital_rotation(), norb=norb, nelec=nelec, copy=copy - ) - -
    -[docs] - def to_parameters(self) -> np.ndarray: - """Convert the operator to a real-valued parameter vector.""" - num_params = len(self.thetas) - params = np.zeros(num_params) - params[: len(self.thetas)] = self.thetas - return params
    - - -
    -[docs] - @staticmethod - def from_parameters( - params: np.ndarray, norb: int, interaction_pairs: list[tuple[int, int]] - ) -> GivensAnsatzOperator: - """Initialize the operator from a real-valued parameter vector. - - Args: - params: The real-valued parameter vector. - norb: The number of spatial orbitals. - interaction_pairs: The orbital pairs to apply the Givens rotation gates to. - """ - return GivensAnsatzOperator( - norb=norb, interaction_pairs=interaction_pairs, thetas=params - )
    - - - def to_orbital_rotation(self) -> np.ndarray: - orbital_rotation = np.eye(self.norb) - for (i, j), theta in zip(self.interaction_pairs[::-1], self.thetas[::-1]): - orbital_rotation[:, j], orbital_rotation[:, i] = drot( - orbital_rotation[:, j], - orbital_rotation[:, i], - math.cos(theta), - math.sin(theta), - ) - return orbital_rotation
    - - -
    [docs] @dataclass(frozen=True) diff --git a/dev/_modules/ffsim/variational/ucj.html b/dev/_modules/ffsim/variational/ucj.html deleted file mode 100644 index 8d790970e..000000000 --- a/dev/_modules/ffsim/variational/ucj.html +++ /dev/null @@ -1,1064 +0,0 @@ - - - - - - - - ffsim.variational.ucj - ffsim 0.0.48.dev0 - - - - - - - - - - - - - - - - - Contents - - - - - - Menu - - - - - - - - - Expand - - - - - - Light mode - - - - - - - - - - - - - - Dark mode - - - - - - - Auto light/dark, in light mode - - - - - - - - - - - - - - - Auto light/dark, in dark mode - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Skip to content - - - -
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    Source code for ffsim.variational.ucj

    -# (C) Copyright IBM 2023.
    -#
    -# This code is licensed under the Apache License, Version 2.0. You may
    -# obtain a copy of this license in the LICENSE.txt file in the root directory
    -# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
    -#
    -# Any modifications or derivative works of this code must retain this
    -# copyright notice, and modified files need to carry a notice indicating
    -# that they have been altered from the originals.
    -
    -"""(Local) unitary cluster Jastrow ansatz."""
    -
    -from __future__ import annotations
    -
    -import itertools
    -from dataclasses import dataclass
    -from typing import cast
    -
    -import numpy as np
    -import scipy.linalg
    -from opt_einsum import contract
    -from typing_extensions import deprecated
    -
    -from ffsim.gates import apply_diag_coulomb_evolution, apply_orbital_rotation
    -from ffsim.linalg import double_factorized_t2
    -from ffsim.variational.util import (
    -    orbital_rotation_from_parameters,
    -    orbital_rotation_to_parameters,
    -)
    -
    -
    -def _validate_diag_coulomb_indices(indices: list[tuple[int, int]] | None):
    -    if indices is not None:
    -        for i, j in indices:
    -            if i > j:
    -                raise ValueError(
    -                    "When specifying diagonal Coulomb indices, you must give only "
    -                    "upper trianglular indices. "
    -                    f"Got {(i, j)}, which is a lower triangular index."
    -                )
    -
    -
    -
    -[docs] -@dataclass(frozen=True) -@deprecated("The UCJOperator class is deprecated. Use UCJOpSpinBalanced instead.") -class UCJOperator: - r"""A unitary cluster Jastrow operator. - - .. warning:: - The UCJOperator class is deprecated. Use :class:`ffsim.UCJOpSpinBalanced` - instead. - - A unitary cluster Jastrow (UCJ) operator has the form - - .. math:: - - \prod_{k = 1}^L \mathcal{W}_k e^{i \mathcal{J}_k} \mathcal{W}_k^\dagger - - where each :math:`\mathcal{W_k}` is an orbital rotation and each :math:`\mathcal{J}` - is a diagonal Coulomb operator of the form - - .. math:: - - \mathcal{J} = \frac12\sum_{\sigma \tau, ij} - \mathbf{J}^{\sigma \tau}_{ij} n_{\sigma, i} n_{\tau, j}. - - In order that the operator commutes with the total spin Z operator, we enforce that - :math:`\mathbf{J}^{\alpha\alpha} = \mathbf{J}^{\beta\beta}` and - :math:`\mathbf{J}^{\alpha\beta} = \mathbf{J}^{\beta\alpha}`. As a result, we have - two sets of matrices for describing the diagonal Coulomb operators: - "alpha-alpha" matrices containing coefficients for terms involving the same spin, - and "alpha-beta" matrices containing coefficients for terms involving different - spins. - - To support variational optimization of the orbital basis, an optional final - orbital rotation can be included in the operator, to be performed at the end. - - Attributes: - diag_coulomb_mats_alpha_alpha (np.ndarray): The "alpha-alpha" diagonal Coulomb - matrices. - diag_coulomb_mats_alpha_beta (np.ndarray): The "alpha-beta" diagonal Coulomb - matrices. - orbital_rotations (np.ndarray): The orbital rotations. - final_orbital_rotation (np.ndarray): The optional final orbital rotation. - """ - - diag_coulomb_mats_alpha_alpha: np.ndarray - diag_coulomb_mats_alpha_beta: np.ndarray - orbital_rotations: np.ndarray - final_orbital_rotation: np.ndarray | None = None - - @property - def norb(self): - """The number of spatial orbitals.""" - return self.diag_coulomb_mats_alpha_alpha.shape[1] - - @property - def n_reps(self): - """The number of ansatz repetitions.""" - return self.diag_coulomb_mats_alpha_alpha.shape[0] - -
    -[docs] - @staticmethod - def n_params( - norb: int, - n_reps: int, - *, - alpha_alpha_indices: list[tuple[int, int]] | None = None, - alpha_beta_indices: list[tuple[int, int]] | None = None, - with_final_orbital_rotation: bool = False, - ) -> int: - """Return the number of parameters of an ansatz with given settings.""" - _validate_diag_coulomb_indices(alpha_alpha_indices) - _validate_diag_coulomb_indices(alpha_beta_indices) - n_params_aa = ( - norb * (norb + 1) // 2 - if alpha_alpha_indices is None - else len(alpha_alpha_indices) - ) - n_params_ab = ( - norb * (norb + 1) // 2 - if alpha_beta_indices is None - else len(alpha_beta_indices) - ) - return ( - n_reps * (norb**2 + n_params_aa + n_params_ab) - + with_final_orbital_rotation * norb**2 - )
    - - -
    -[docs] - @staticmethod - @deprecated("The UCJOperator class is deprecated. Use UCJOpSpinBalanced instead.") - def from_parameters( - params: np.ndarray, - *, - norb: int, - n_reps: int, - alpha_alpha_indices: list[tuple[int, int]] | None = None, - alpha_beta_indices: list[tuple[int, int]] | None = None, - with_final_orbital_rotation: bool = False, - ) -> UCJOperator: - r"""Initialize the UCJ operator from a real-valued parameter vector. - - Args: - params: The real-valued parameter vector. - norb: The number of spatial orbitals. - n_reps: The number of ansatz repetitions (:math:`L` from the docstring of - this class). - alpha_alpha_indices: Allowed indices for nonzero values of the "alpha-alpha" - diagonal Coulomb matrices (see the docstring of this class). - If not specified, all matrix entries are allowed to be nonzero. - This list should contain only upper trianglular indices, i.e., - pairs :math:`(i, j)` where :math:`i \leq j`. Passing a list with - lower triangular indices will raise an error. - alpha_beta_indices: Allowed indices for nonzero values of the "alpha-beta" - diagonal Coulomb matrices (see the docstring of this class). - If not specified, all matrix entries are allowed to be nonzero. - This list should contain only upper trianglular indices, i.e., - pairs :math:`(i, j)` where :math:`i \leq j`. Passing a list with - lower triangular indices will raise an error. - with_final_orbital_rotation: Whether to include a final orbital rotation - in the operator. - - Returns: - The UCJ operator constructed from the given parameters. - - Raises: - ValueError: alpha_alpha_indices contains lower triangular indices. - ValueError: alpha_beta_indices contains lower triangular indices. - """ - _validate_diag_coulomb_indices(alpha_alpha_indices) - _validate_diag_coulomb_indices(alpha_beta_indices) - return UCJOperator( - *_ucj_from_parameters( - params, - norb=norb, - n_reps=n_reps, - alpha_alpha_indices=alpha_alpha_indices, - alpha_beta_indices=alpha_beta_indices, - with_final_orbital_rotation=with_final_orbital_rotation, - ) - )
    - - -
    -[docs] - def to_parameters( - self, - *, - alpha_alpha_indices: list[tuple[int, int]] | None = None, - alpha_beta_indices: list[tuple[int, int]] | None = None, - ) -> np.ndarray: - r"""Convert the UCJ operator to a real-valued parameter vector. - - If `alpha_alpha_indices` or `alpha_beta_indices` is specified, the returned - parameter vector will incorporate only the diagonal Coulomb matrix entries - corresponding to the given indices, so the original operator will not be - recoverable from the parameter vector. - - Args: - alpha_alpha_indices: Allowed indices for nonzero values of the "alpha-alpha" - diagonal Coulomb matrices (see the docstring of this class). - If not specified, all matrix entries are allowed to be nonzero. - This list should contain only upper trianglular indices, i.e., - pairs :math:`(i, j)` where :math:`i \leq j`. Passing a list with - lower triangular indices will raise an error. - alpha_beta_indices: Allowed indices for nonzero values of the "alpha-beta" - diagonal Coulomb matrices (see the docstring of this class). - If not specified, all matrix entries are allowed to be nonzero. - This list should contain only upper trianglular indices, i.e., - pairs :math:`(i, j)` where :math:`i \leq j`. Passing a list with - lower triangular indices will raise an error. - - Returns: - The real-valued parameter vector. - - Raises: - ValueError: alpha_alpha_indices contains lower triangular indices. - ValueError: alpha_beta_indices contains lower triangular indices. - """ - _validate_diag_coulomb_indices(alpha_alpha_indices) - _validate_diag_coulomb_indices(alpha_beta_indices) - return _ucj_to_parameters( - diag_coulomb_mats_alpha_alpha=self.diag_coulomb_mats_alpha_alpha, - diag_coulomb_mats_alpha_beta=self.diag_coulomb_mats_alpha_beta, - orbital_rotations=self.orbital_rotations, - final_orbital_rotation=self.final_orbital_rotation, - alpha_alpha_indices=alpha_alpha_indices, - alpha_beta_indices=alpha_beta_indices, - )
    - - -
    -[docs] - @staticmethod - @deprecated("The UCJOperator class is deprecated. Use UCJOpSpinBalanced instead.") - def from_t_amplitudes( - t2: np.ndarray, - *, - t1: np.ndarray | None = None, - n_reps: int | None = None, - tol: float = 1e-8, - ) -> UCJOperator: - """Initialize the UCJ operator from t2 (and optionally t1) amplitudes.""" - nocc, _, nvrt, _ = t2.shape - norb = nocc + nvrt - - diag_coulomb_mats, orbital_rotations = double_factorized_t2(t2, tol=tol) - diag_coulomb_mats = diag_coulomb_mats.reshape(-1, norb, norb)[:n_reps] - orbital_rotations = orbital_rotations.reshape(-1, norb, norb)[:n_reps] - - final_orbital_rotation = None - if t1 is not None: - final_orbital_rotation_generator = np.zeros((norb, norb), dtype=complex) - final_orbital_rotation_generator[:nocc, nocc:] = t1 - final_orbital_rotation_generator[nocc:, :nocc] = -t1.T - final_orbital_rotation = scipy.linalg.expm(final_orbital_rotation_generator) - - return UCJOperator( - diag_coulomb_mats_alpha_alpha=diag_coulomb_mats, - diag_coulomb_mats_alpha_beta=diag_coulomb_mats, - orbital_rotations=orbital_rotations, - final_orbital_rotation=final_orbital_rotation, - )
    - - -
    -[docs] - def to_t_amplitudes( - self, nocc: int | None = None - ) -> np.ndarray | tuple[np.ndarray, np.ndarray]: - """Convert the UCJ operator to t2 (and possibly t1) amplitudes.""" - t2 = ( - 1j - * contract( - "kpq,kap,kip,kbq,kjq->ijab", - self.diag_coulomb_mats_alpha_alpha, - self.orbital_rotations, - self.orbital_rotations.conj(), - self.orbital_rotations, - self.orbital_rotations.conj(), - optimize="greedy", - )[:nocc, :nocc, nocc:, nocc:] - ) - - if self.final_orbital_rotation is None: - return t2 - - t1 = scipy.linalg.logm(self.final_orbital_rotation)[:nocc, nocc:] - return t2, t1
    - - - def _apply_unitary_( - self, vec: np.ndarray, norb: int, nelec: int | tuple[int, int], copy: bool - ) -> np.ndarray: - """Apply the operator to a vector.""" - if isinstance(nelec, int): - return NotImplemented - if copy: - vec = vec.copy() - for mat, mat_alpha_beta, orbital_rotation in zip( - self.diag_coulomb_mats_alpha_alpha, - self.diag_coulomb_mats_alpha_beta, - self.orbital_rotations, - ): - vec = apply_diag_coulomb_evolution( - vec, - mat=(mat, mat_alpha_beta, mat), - time=-1.0, - norb=norb, - nelec=nelec, - orbital_rotation=orbital_rotation, - copy=False, - ) - if self.final_orbital_rotation is not None: - vec = apply_orbital_rotation( - vec, - mat=self.final_orbital_rotation, - norb=norb, - nelec=nelec, - copy=False, - ) - return vec
    - - - -
    -[docs] -@dataclass -@deprecated("The RealUCJOperator class is deprecated. Use UCJOpSpinBalanced instead.") -class RealUCJOperator: - r"""Real-valued unitary cluster Jastrow operator. - - .. warning:: - The RealUCJOperator class is deprecated. Use :class:`ffsim.UCJOpSpinBalanced` - instead. - - A real-valued unitary cluster Jastrow (UCJ) operator has the form - - .. math:: - - \prod_{k = 1}^L - \mathcal{W_k^*} e^{i \mathcal{-J}_k} \mathcal{W_k}^T - \mathcal{W_k} e^{i \mathcal{J}_k} \mathcal{W_k^\dagger} - - where each :math:`\mathcal{W_k}` is an orbital rotation and each :math:`\mathcal{J}` - is a diagonal Coulomb operator of the form - - .. math:: - - \mathcal{J} = \frac12\sum_{ij,\sigma \tau} - \mathbf{J}^{\sigma \tau}_{ij} n_{i,\sigma} n_{j,\tau}. - - In order that the operator commutes with the total spin Z operator, we enforce that - :math:`\mathbf{J}^{\alpha\alpha} = \mathbf{J}^{\beta\beta}` and - :math:`\mathbf{J}^{\alpha\beta} = \mathbf{J}^{\beta\alpha}`. As a result, we have - two sets of matrices for describing the diagonal Coulomb operators: - "alpha-alpha" matrices containing coefficients for terms involving the same spin, - and "alpha-beta" matrices containing coefficients for terms involving different - spins. - - To support variational optimization of the orbital basis, an optional final - orbital rotation can be included in the operator, to be performed at the end. - - Attributes: - diag_coulomb_mats_alpha_alpha: The "alpha-alpha" diagonal Coulomb matrices. - diag_coulomb_mats_alpha_beta: The "alpha-beta" diagonal Coulomb matrices. - orbital_rotations: The orbital rotations. - final_orbital_rotation: The optional final orbital rotation. - """ - - diag_coulomb_mats_alpha_alpha: np.ndarray - diag_coulomb_mats_alpha_beta: np.ndarray - orbital_rotations: np.ndarray - final_orbital_rotation: np.ndarray | None = None - - @property - def norb(self): - """The number of spatial orbitals.""" - return self.diag_coulomb_mats_alpha_alpha.shape[1] - - @property - def n_reps(self): - """The number of ansatz repetitions.""" - return self.diag_coulomb_mats_alpha_alpha.shape[0] - -
    -[docs] - @staticmethod - def n_params( - norb: int, - n_reps: int, - *, - alpha_alpha_indices: list[tuple[int, int]] | None = None, - alpha_beta_indices: list[tuple[int, int]] | None = None, - with_final_orbital_rotation: bool = False, - ) -> int: - """Return the number of parameters of an ansatz with given settings.""" - n_params_aa = ( - norb * (norb + 1) // 2 - if alpha_alpha_indices is None - else len(alpha_alpha_indices) - ) - n_params_ab = ( - norb * (norb + 1) // 2 - if alpha_beta_indices is None - else len(alpha_beta_indices) - ) - return ( - n_reps * (norb**2 + n_params_aa + n_params_ab) - + with_final_orbital_rotation * norb**2 - )
    - - -
    -[docs] - @staticmethod - @deprecated( - "The RealUCJOperator class is deprecated. Use UCJOpSpinBalanced instead." - ) - def from_parameters( - params: np.ndarray, - *, - norb: int, - n_reps: int, - alpha_alpha_indices: list[tuple[int, int]] | None = None, - alpha_beta_indices: list[tuple[int, int]] | None = None, - with_final_orbital_rotation: bool = False, - ) -> "RealUCJOperator": - r"""Initialize the real UCJ operator from a real-valued parameter vector. - - Args: - params: The real-valued parameter vector. - norb: The number of spatial orbitals. - n_reps: The number of ansatz repetitions (:math:`L` from the docstring of - this class). - alpha_alpha_indices: Allowed indices for nonzero values of the "alpha-alpha" - diagonal Coulomb matrices (see the docstring of this class). - If not specified, all matrix entries are allowed to be nonzero. - This list should contain only upper trianglular indices, i.e., - pairs :math:`(i, j)` where :math:`i \leq j`. Passing a list with - lower triangular indices will raise an error. - alpha_beta_indices: Allowed indices for nonzero values of the "alpha-beta" - diagonal Coulomb matrices (see the docstring of this class). - If not specified, all matrix entries are allowed to be nonzero. - This list should contain only upper trianglular indices, i.e., - pairs :math:`(i, j)` where :math:`i \leq j`. Passing a list with - lower triangular indices will raise an error. - with_final_orbital_rotation: Whether to include a final orbital rotation - in the operator. - - Returns: - The real UCJ operator constructed from the given parameters. - - Raises: - ValueError: alpha_alpha_indices contains lower triangular indices. - ValueError: alpha_beta_indices contains lower triangular indices. - """ - _validate_diag_coulomb_indices(alpha_alpha_indices) - _validate_diag_coulomb_indices(alpha_beta_indices) - return RealUCJOperator( - *_ucj_from_parameters( - params, - norb=norb, - n_reps=n_reps, - alpha_alpha_indices=alpha_alpha_indices, - alpha_beta_indices=alpha_beta_indices, - with_final_orbital_rotation=with_final_orbital_rotation, - ) - )
    - - -
    -[docs] - def to_parameters( - self, - *, - alpha_alpha_indices: list[tuple[int, int]] | None = None, - alpha_beta_indices: list[tuple[int, int]] | None = None, - ) -> np.ndarray: - r"""Convert the real UCJ operator to a real-valued parameter vector. - - If `alpha_alpha_indices` or `alpha_beta_indices` is specified, the returned - parameter vector will incorporate only the diagonal Coulomb matrix entries - corresponding to the given indices, so the original operator will not be - recoverable from the parameter vector. - - Args: - alpha_alpha_indices: Allowed indices for nonzero values of the "alpha-alpha" - diagonal Coulomb matrices (see the docstring of this class). - If not specified, all matrix entries are allowed to be nonzero. - This list should contain only upper trianglular indices, i.e., - pairs :math:`(i, j)` where :math:`i \leq j`. Passing a list with - lower triangular indices will raise an error. - alpha_beta_indices: Allowed indices for nonzero values of the "alpha-beta" - diagonal Coulomb matrices (see the docstring of this class). - If not specified, all matrix entries are allowed to be nonzero. - This list should contain only upper trianglular indices, i.e., - pairs :math:`(i, j)` where :math:`i \leq j`. Passing a list with - lower triangular indices will raise an error. - - Returns: - The real-valued parameter vector. - - Raises: - ValueError: alpha_alpha_indices contains lower triangular indices. - ValueError: alpha_beta_indices contains lower triangular indices. - """ - _validate_diag_coulomb_indices(alpha_alpha_indices) - _validate_diag_coulomb_indices(alpha_beta_indices) - return _ucj_to_parameters( - diag_coulomb_mats_alpha_alpha=self.diag_coulomb_mats_alpha_alpha, - diag_coulomb_mats_alpha_beta=self.diag_coulomb_mats_alpha_beta, - orbital_rotations=self.orbital_rotations, - final_orbital_rotation=self.final_orbital_rotation, - alpha_alpha_indices=alpha_alpha_indices, - alpha_beta_indices=alpha_beta_indices, - )
    - - -
    -[docs] - @staticmethod - @deprecated( - "The RealUCJOperator class is deprecated. Use UCJOpSpinBalanced instead." - ) - def from_t_amplitudes( - t2: np.ndarray, - *, - t1: np.ndarray | None = None, - n_reps: int | None = None, - tol: float = 1e-8, - ) -> "RealUCJOperator": - """Initialize the real UCJ operator from t2 (and optionally t1) amplitudes.""" - nocc, _, nvrt, _ = t2.shape - norb = nocc + nvrt - - diag_coulomb_mats, orbital_rotations = double_factorized_t2(t2, tol=tol) - diag_coulomb_mats = diag_coulomb_mats[:n_reps, 0] - orbital_rotations = orbital_rotations[:n_reps, 0] - - final_orbital_rotation = None - if t1 is not None: - final_orbital_rotation_generator = np.zeros((norb, norb), dtype=complex) - final_orbital_rotation_generator[:nocc, nocc:] = t1 - final_orbital_rotation_generator[nocc:, :nocc] = -t1.T - final_orbital_rotation = scipy.linalg.expm(final_orbital_rotation_generator) - - return RealUCJOperator( - diag_coulomb_mats_alpha_alpha=diag_coulomb_mats, - diag_coulomb_mats_alpha_beta=diag_coulomb_mats, - orbital_rotations=orbital_rotations, - final_orbital_rotation=final_orbital_rotation, - )
    - - -
    -[docs] - def to_t_amplitudes( - self, nocc: int | None = None - ) -> np.ndarray | tuple[np.ndarray, np.ndarray]: - """Convert the UCJ operator to t2 (and possibly t1) amplitudes.""" - t2 = 1j * ( - contract( - "kpq,kap,kip,kbq,kjq->ijab", - self.diag_coulomb_mats_alpha_alpha, - self.orbital_rotations, - self.orbital_rotations.conj(), - self.orbital_rotations, - self.orbital_rotations.conj(), - optimize="greedy", - )[:nocc, :nocc, nocc:, nocc:] - + contract( - "kpq,kap,kip,kbq,kjq->ijab", - -self.diag_coulomb_mats_alpha_alpha, - self.orbital_rotations.conj(), - self.orbital_rotations, - self.orbital_rotations.conj(), - self.orbital_rotations, - optimize="greedy", - )[:nocc, :nocc, nocc:, nocc:] - ) - - if self.final_orbital_rotation is None: - return t2 - - t1 = scipy.linalg.logm(self.final_orbital_rotation)[:nocc, nocc:] - return t2, t1
    - - - def _apply_unitary_( - self, vec: np.ndarray, norb: int, nelec: int | tuple[int, int], copy: bool - ) -> np.ndarray: - """Apply the operator to a vector.""" - if isinstance(nelec, int): - return NotImplemented - if copy: - vec = vec.copy() - for mat, mat_alpha_beta, orbital_rotation in zip( - self.diag_coulomb_mats_alpha_alpha, - self.diag_coulomb_mats_alpha_beta, - self.orbital_rotations, - ): - vec = apply_diag_coulomb_evolution( - vec, - mat=(mat, mat_alpha_beta, mat), - time=-1.0, - norb=norb, - nelec=nelec, - orbital_rotation=orbital_rotation, - copy=False, - ) - vec = apply_diag_coulomb_evolution( - vec, - mat=(-mat, -mat_alpha_beta, -mat), - time=-1.0, - norb=norb, - nelec=nelec, - orbital_rotation=orbital_rotation.conj(), - copy=False, - ) - if self.final_orbital_rotation is not None: - vec = apply_orbital_rotation( - vec, - mat=self.final_orbital_rotation, - norb=norb, - nelec=nelec, - copy=False, - ) - return vec
    - - - -def _ucj_from_parameters( - params: np.ndarray, - *, - norb: int, - n_reps: int, - alpha_alpha_indices: list[tuple[int, int]] | None = None, - alpha_beta_indices: list[tuple[int, int]] | None = None, - with_final_orbital_rotation: bool = False, -) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray | None]: - triu_indices = cast( - list[tuple[int, int]], - list(itertools.combinations_with_replacement(range(norb), 2)), - ) - if alpha_alpha_indices is None: - alpha_alpha_indices = triu_indices - if alpha_beta_indices is None: - alpha_beta_indices = triu_indices - diag_coulomb_mats_alpha_alpha = np.zeros((n_reps, norb, norb)) - diag_coulomb_mats_alpha_beta = np.zeros((n_reps, norb, norb)) - orbital_rotations = np.zeros((n_reps, norb, norb), dtype=complex) - index = 0 - for ( - orbital_rotation, - diag_coulomb_mat_alpha_alpha, - diag_coulomb_mat_alpha_beta, - ) in zip( - orbital_rotations, - diag_coulomb_mats_alpha_alpha, - diag_coulomb_mats_alpha_beta, - ): - # orbital rotation - n_params = norb**2 - orbital_rotation[:] = orbital_rotation_from_parameters( - params[index : index + n_params], norb - ) - index += n_params - # diag coulomb matrix, alpha-alpha - if alpha_alpha_indices: - n_params = len(alpha_alpha_indices) - rows, cols = zip(*alpha_alpha_indices) - vals = params[index : index + n_params] - diag_coulomb_mat_alpha_alpha[rows, cols] = vals - diag_coulomb_mat_alpha_alpha[cols, rows] = vals - index += n_params - # diag coulomb matrix, alpha-beta - if alpha_beta_indices: - n_params = len(alpha_beta_indices) - rows, cols = zip(*alpha_beta_indices) - vals = params[index : index + n_params] - diag_coulomb_mat_alpha_beta[rows, cols] = vals - diag_coulomb_mat_alpha_beta[cols, rows] = vals - index += n_params - # final orbital rotation - final_orbital_rotation = None - if with_final_orbital_rotation: - final_orbital_rotation = orbital_rotation_from_parameters(params[index:], norb) - return ( - diag_coulomb_mats_alpha_alpha, - diag_coulomb_mats_alpha_beta, - orbital_rotations, - final_orbital_rotation, - ) - - -def _ucj_to_parameters( - diag_coulomb_mats_alpha_alpha: np.ndarray, - diag_coulomb_mats_alpha_beta: np.ndarray, - orbital_rotations: np.ndarray, - final_orbital_rotation: np.ndarray | None = None, - *, - alpha_alpha_indices: list[tuple[int, int]] | None = None, - alpha_beta_indices: list[tuple[int, int]] | None = None, -) -> np.ndarray: - n_reps, norb, _ = diag_coulomb_mats_alpha_alpha.shape - triu_indices = cast( - list[tuple[int, int]], - list(itertools.combinations_with_replacement(range(norb), 2)), - ) - if alpha_alpha_indices is None: - alpha_alpha_indices = triu_indices - if alpha_beta_indices is None: - alpha_beta_indices = triu_indices - ntheta = n_reps * (len(alpha_alpha_indices) + len(alpha_beta_indices) + norb**2) - if final_orbital_rotation is not None: - ntheta += norb**2 - theta = np.zeros(ntheta) - index = 0 - for ( - orbital_rotation, - diag_coulomb_mat_alpha_alpha, - diag_coulomb_mat_alpha_beta, - ) in zip( - orbital_rotations, - diag_coulomb_mats_alpha_alpha, - diag_coulomb_mats_alpha_beta, - ): - # orbital rotation - n_params = norb**2 - theta[index : index + n_params] = orbital_rotation_to_parameters( - orbital_rotation - ) - index += n_params - # diag coulomb matrix, alpha-alpha - if alpha_alpha_indices: - n_params = len(alpha_alpha_indices) - theta[index : index + n_params] = diag_coulomb_mat_alpha_alpha[ - tuple(zip(*alpha_alpha_indices)) - ] - index += n_params - # diag coulomb matrix, alpha-beta - if alpha_beta_indices: - n_params = len(alpha_beta_indices) - theta[index : index + n_params] = diag_coulomb_mat_alpha_beta[ - tuple(zip(*alpha_beta_indices)) - ] - index += n_params - # final orbital rotation - if final_orbital_rotation is not None: - theta[index : index + norb**2] = orbital_rotation_to_parameters( - final_orbital_rotation - ) - return theta -
    -
    -
    -
    - - -
    -
    - - Made with Sphinx and @pradyunsg's - - Furo - -
    -
    - -
    -
    - -
    -
    - -
    -
    - - - - - - \ No newline at end of file diff --git a/dev/_modules/index.html b/dev/_modules/index.html index 4df43827c..2a850fe82 100644 --- a/dev/_modules/index.html +++ b/dev/_modules/index.html @@ -300,7 +300,6 @@

    All modules for which code is available

  1. ffsim.qiskit.gates.orbital_rotation
  2. ffsim.qiskit.gates.slater_determinant
  3. ffsim.qiskit.gates.ucj
  4. -
  5. ffsim.qiskit.gates.ucj_operator
  6. ffsim.qiskit.jordan_wigner
  7. ffsim.qiskit.sampler
  8. ffsim.qiskit.sim
  9. @@ -326,7 +325,6 @@

    All modules for which code is available

  10. ffsim.variational.multireference
  11. ffsim.variational.num_num
  12. ffsim.variational.uccsd
  13. -
  14. ffsim.variational.ucj
  15. ffsim.variational.ucj_spin_balanced
  16. ffsim.variational.ucj_spin_unbalanced
  17. ffsim.variational.ucj_spinless
  18. diff --git a/dev/api/ffsim.html b/dev/api/ffsim.html index c993baccb..df8265766 100644 --- a/dev/api/ffsim.html +++ b/dev/api/ffsim.html @@ -880,83 +880,6 @@ -
    -
    -class ffsim.GivensAnsatzOperator(norb, interaction_pairs, thetas)[source]
    -

    Bases: object

    -

    A Givens rotation ansatz operator.

    -
    -

    Warning

    -

    This class is deprecated. Use ffsim.GivensAnsatzOp instead.

    -
    -

    The Givens rotation ansatz consists of a sequence of Givens rotations.

    -

    Note that this ansatz does not implement any interactions between spin alpha and -spin beta orbitals.

    -
    -
    -norb
    -

    The number of spatial orbitals.

    -
    -
    Type:
    -

    int

    -
    -
    -
    - -
    -
    -interaction_pairs
    -

    The orbital pairs to apply the Givens -rotations to.

    -
    -
    Type:
    -

    list[tuple[int, int]]

    -
    -
    -
    - -
    -
    -thetas
    -

    The angles for the Givens rotations.

    -
    -
    Type:
    -

    np.ndarray

    -
    -
    -
    - -
    -
    -static from_parameters(params, norb, interaction_pairs)[source]
    -

    Initialize the operator from a real-valued parameter vector.

    -
    -
    Parameters:
    -
      -
    • params (ndarray) – The real-valued parameter vector.

    • -
    • norb (int) – The number of spatial orbitals.

    • -
    • interaction_pairs (list[tuple[int, int]]) – The orbital pairs to apply the Givens rotation gates to.

    • -
    -
    -
    Return type:
    -

    GivensAnsatzOperator

    -
    -
    -
    - -
    -
    -to_parameters()[source]
    -

    Convert the operator to a real-valued parameter vector.

    -
    -
    Return type:
    -

    ndarray

    -
    -
    -
    - -
    -
    class ffsim.HopGateAnsatzOperator(norb, interaction_pairs, thetas, final_orbital_rotation=None)[source]
    @@ -1793,192 +1716,6 @@
    -
    -
    -class ffsim.RealUCJOperator(diag_coulomb_mats_alpha_alpha, diag_coulomb_mats_alpha_beta, orbital_rotations, final_orbital_rotation=None)[source]
    -

    Bases: object

    -

    Real-valued unitary cluster Jastrow operator.

    -
    -

    Warning

    -

    The RealUCJOperator class is deprecated. Use ffsim.UCJOpSpinBalanced -instead.

    -
    -

    A real-valued unitary cluster Jastrow (UCJ) operator has the form

    -
    -
    -\[\prod_{k = 1}^L -\mathcal{W_k^*} e^{i \mathcal{-J}_k} \mathcal{W_k}^T -\mathcal{W_k} e^{i \mathcal{J}_k} \mathcal{W_k^\dagger}\]
    -
    -

    where each \(\mathcal{W_k}\) is an orbital rotation and each \(\mathcal{J}\) -is a diagonal Coulomb operator of the form

    -
    -
    -\[\mathcal{J} = \frac12\sum_{ij,\sigma \tau} -\mathbf{J}^{\sigma \tau}_{ij} n_{i,\sigma} n_{j,\tau}.\]
    -
    -

    In order that the operator commutes with the total spin Z operator, we enforce that -\(\mathbf{J}^{\alpha\alpha} = \mathbf{J}^{\beta\beta}\) and -\(\mathbf{J}^{\alpha\beta} = \mathbf{J}^{\beta\alpha}\). As a result, we have -two sets of matrices for describing the diagonal Coulomb operators: -“alpha-alpha” matrices containing coefficients for terms involving the same spin, -and “alpha-beta” matrices containing coefficients for terms involving different -spins.

    -

    To support variational optimization of the orbital basis, an optional final -orbital rotation can be included in the operator, to be performed at the end.

    -
    -
    -diag_coulomb_mats_alpha_alpha
    -

    The “alpha-alpha” diagonal Coulomb matrices.

    -
    - -
    -
    -diag_coulomb_mats_alpha_beta
    -

    The “alpha-beta” diagonal Coulomb matrices.

    -
    - -
    -
    -orbital_rotations
    -

    The orbital rotations.

    -
    - -
    -
    -final_orbital_rotation
    -

    The optional final orbital rotation.

    -
    - -
    -
    -static from_parameters(params, *, norb, n_reps, alpha_alpha_indices=None, alpha_beta_indices=None, with_final_orbital_rotation=False)[source]
    -

    Initialize the real UCJ operator from a real-valued parameter vector.

    -
    -
    Parameters:
    -
      -
    • params (ndarray) – The real-valued parameter vector.

    • -
    • norb (int) – The number of spatial orbitals.

    • -
    • n_reps (int) – The number of ansatz repetitions (\(L\) from the docstring of -this class).

    • -
    • alpha_alpha_indices (list[tuple[int, int]] | None) – Allowed indices for nonzero values of the “alpha-alpha” -diagonal Coulomb matrices (see the docstring of this class). -If not specified, all matrix entries are allowed to be nonzero. -This list should contain only upper trianglular indices, i.e., -pairs \((i, j)\) where \(i \leq j\). Passing a list with -lower triangular indices will raise an error.

    • -
    • alpha_beta_indices (list[tuple[int, int]] | None) – Allowed indices for nonzero values of the “alpha-beta” -diagonal Coulomb matrices (see the docstring of this class). -If not specified, all matrix entries are allowed to be nonzero. -This list should contain only upper trianglular indices, i.e., -pairs \((i, j)\) where \(i \leq j\). Passing a list with -lower triangular indices will raise an error.

    • -
    • with_final_orbital_rotation (bool) – Whether to include a final orbital rotation -in the operator.

    • -
    -
    -
    Return type:
    -

    RealUCJOperator

    -
    -
    Returns:
    -

    The real UCJ operator constructed from the given parameters.

    -
    -
    Raises:
    -
      -
    • ValueError – alpha_alpha_indices contains lower triangular indices.

    • -
    • ValueError – alpha_beta_indices contains lower triangular indices.

    • -
    -
    -
    -
    - -
    -
    -static from_t_amplitudes(t2, *, t1=None, n_reps=None, tol=1e-08)[source]
    -

    Initialize the real UCJ operator from t2 (and optionally t1) amplitudes.

    -
    -
    Return type:
    -

    RealUCJOperator

    -
    -
    -
    - -
    -
    -static n_params(norb, n_reps, *, alpha_alpha_indices=None, alpha_beta_indices=None, with_final_orbital_rotation=False)[source]
    -

    Return the number of parameters of an ansatz with given settings.

    -
    -
    Return type:
    -

    int

    -
    -
    -
    - -
    -
    -property n_reps
    -

    The number of ansatz repetitions.

    -
    - -
    -
    -property norb
    -

    The number of spatial orbitals.

    -
    - -
    -
    -to_parameters(*, alpha_alpha_indices=None, alpha_beta_indices=None)[source]
    -

    Convert the real UCJ operator to a real-valued parameter vector.

    -

    If alpha_alpha_indices or alpha_beta_indices is specified, the returned -parameter vector will incorporate only the diagonal Coulomb matrix entries -corresponding to the given indices, so the original operator will not be -recoverable from the parameter vector.

    -
    -
    Parameters:
    -
      -
    • alpha_alpha_indices (list[tuple[int, int]] | None) – Allowed indices for nonzero values of the “alpha-alpha” -diagonal Coulomb matrices (see the docstring of this class). -If not specified, all matrix entries are allowed to be nonzero. -This list should contain only upper trianglular indices, i.e., -pairs \((i, j)\) where \(i \leq j\). Passing a list with -lower triangular indices will raise an error.

    • -
    • alpha_beta_indices (list[tuple[int, int]] | None) – Allowed indices for nonzero values of the “alpha-beta” -diagonal Coulomb matrices (see the docstring of this class). -If not specified, all matrix entries are allowed to be nonzero. -This list should contain only upper trianglular indices, i.e., -pairs \((i, j)\) where \(i \leq j\). Passing a list with -lower triangular indices will raise an error.

    • -
    -
    -
    Return type:
    -

    ndarray

    -
    -
    Returns:
    -

    The real-valued parameter vector.

    -
    -
    Raises:
    -
      -
    • ValueError – alpha_alpha_indices contains lower triangular indices.

    • -
    • ValueError – alpha_beta_indices contains lower triangular indices.

    • -
    -
    -
    -
    - -
    -
    -to_t_amplitudes(nocc=None)[source]
    -

    Convert the UCJ operator to t2 (and possibly t1) amplitudes.

    -
    -
    Return type:
    -

    ndarray | tuple[ndarray, ndarray]

    -
    -
    -
    - -
    -
    class ffsim.SingleFactorizedHamiltonian(one_body_tensor, one_body_squares, constant=0.0)[source]
    @@ -3063,212 +2800,6 @@
    -
    -
    -class ffsim.UCJOperator(diag_coulomb_mats_alpha_alpha, diag_coulomb_mats_alpha_beta, orbital_rotations, final_orbital_rotation=None)[source]
    -

    Bases: object

    -

    A unitary cluster Jastrow operator.

    -
    -

    Warning

    -

    The UCJOperator class is deprecated. Use ffsim.UCJOpSpinBalanced -instead.

    -
    -

    A unitary cluster Jastrow (UCJ) operator has the form

    -
    -
    -\[\prod_{k = 1}^L \mathcal{W}_k e^{i \mathcal{J}_k} \mathcal{W}_k^\dagger\]
    -
    -

    where each \(\mathcal{W_k}\) is an orbital rotation and each \(\mathcal{J}\) -is a diagonal Coulomb operator of the form

    -
    -
    -\[\mathcal{J} = \frac12\sum_{\sigma \tau, ij} -\mathbf{J}^{\sigma \tau}_{ij} n_{\sigma, i} n_{\tau, j}.\]
    -
    -

    In order that the operator commutes with the total spin Z operator, we enforce that -\(\mathbf{J}^{\alpha\alpha} = \mathbf{J}^{\beta\beta}\) and -\(\mathbf{J}^{\alpha\beta} = \mathbf{J}^{\beta\alpha}\). As a result, we have -two sets of matrices for describing the diagonal Coulomb operators: -“alpha-alpha” matrices containing coefficients for terms involving the same spin, -and “alpha-beta” matrices containing coefficients for terms involving different -spins.

    -

    To support variational optimization of the orbital basis, an optional final -orbital rotation can be included in the operator, to be performed at the end.

    -
    -
    -diag_coulomb_mats_alpha_alpha
    -

    The “alpha-alpha” diagonal Coulomb -matrices.

    -
    -
    Type:
    -

    np.ndarray

    -
    -
    -
    - -
    -
    -diag_coulomb_mats_alpha_beta
    -

    The “alpha-beta” diagonal Coulomb -matrices.

    -
    -
    Type:
    -

    np.ndarray

    -
    -
    -
    - -
    -
    -orbital_rotations
    -

    The orbital rotations.

    -
    -
    Type:
    -

    np.ndarray

    -
    -
    -
    - -
    -
    -final_orbital_rotation
    -

    The optional final orbital rotation.

    -
    -
    Type:
    -

    np.ndarray

    -
    -
    -
    - -
    -
    -static from_parameters(params, *, norb, n_reps, alpha_alpha_indices=None, alpha_beta_indices=None, with_final_orbital_rotation=False)[source]
    -

    Initialize the UCJ operator from a real-valued parameter vector.

    -
    -
    Parameters:
    -
      -
    • params (ndarray) – The real-valued parameter vector.

    • -
    • norb (int) – The number of spatial orbitals.

    • -
    • n_reps (int) – The number of ansatz repetitions (\(L\) from the docstring of -this class).

    • -
    • alpha_alpha_indices (list[tuple[int, int]] | None) – Allowed indices for nonzero values of the “alpha-alpha” -diagonal Coulomb matrices (see the docstring of this class). -If not specified, all matrix entries are allowed to be nonzero. -This list should contain only upper trianglular indices, i.e., -pairs \((i, j)\) where \(i \leq j\). Passing a list with -lower triangular indices will raise an error.

    • -
    • alpha_beta_indices (list[tuple[int, int]] | None) – Allowed indices for nonzero values of the “alpha-beta” -diagonal Coulomb matrices (see the docstring of this class). -If not specified, all matrix entries are allowed to be nonzero. -This list should contain only upper trianglular indices, i.e., -pairs \((i, j)\) where \(i \leq j\). Passing a list with -lower triangular indices will raise an error.

    • -
    • with_final_orbital_rotation (bool) – Whether to include a final orbital rotation -in the operator.

    • -
    -
    -
    Return type:
    -

    UCJOperator

    -
    -
    Returns:
    -

    The UCJ operator constructed from the given parameters.

    -
    -
    Raises:
    -
      -
    • ValueError – alpha_alpha_indices contains lower triangular indices.

    • -
    • ValueError – alpha_beta_indices contains lower triangular indices.

    • -
    -
    -
    -
    - -
    -
    -static from_t_amplitudes(t2, *, t1=None, n_reps=None, tol=1e-08)[source]
    -

    Initialize the UCJ operator from t2 (and optionally t1) amplitudes.

    -
    -
    Return type:
    -

    UCJOperator

    -
    -
    -
    - -
    -
    -static n_params(norb, n_reps, *, alpha_alpha_indices=None, alpha_beta_indices=None, with_final_orbital_rotation=False)[source]
    -

    Return the number of parameters of an ansatz with given settings.

    -
    -
    Return type:
    -

    int

    -
    -
    -
    - -
    -
    -property n_reps
    -

    The number of ansatz repetitions.

    -
    - -
    -
    -property norb
    -

    The number of spatial orbitals.

    -
    - -
    -
    -to_parameters(*, alpha_alpha_indices=None, alpha_beta_indices=None)[source]
    -

    Convert the UCJ operator to a real-valued parameter vector.

    -

    If alpha_alpha_indices or alpha_beta_indices is specified, the returned -parameter vector will incorporate only the diagonal Coulomb matrix entries -corresponding to the given indices, so the original operator will not be -recoverable from the parameter vector.

    -
    -
    Parameters:
    -
      -
    • alpha_alpha_indices (list[tuple[int, int]] | None) – Allowed indices for nonzero values of the “alpha-alpha” -diagonal Coulomb matrices (see the docstring of this class). -If not specified, all matrix entries are allowed to be nonzero. -This list should contain only upper trianglular indices, i.e., -pairs \((i, j)\) where \(i \leq j\). Passing a list with -lower triangular indices will raise an error.

    • -
    • alpha_beta_indices (list[tuple[int, int]] | None) – Allowed indices for nonzero values of the “alpha-beta” -diagonal Coulomb matrices (see the docstring of this class). -If not specified, all matrix entries are allowed to be nonzero. -This list should contain only upper trianglular indices, i.e., -pairs \((i, j)\) where \(i \leq j\). Passing a list with -lower triangular indices will raise an error.

    • -
    -
    -
    Return type:
    -

    ndarray

    -
    -
    Returns:
    -

    The real-valued parameter vector.

    -
    -
    Raises:
    -
      -
    • ValueError – alpha_alpha_indices contains lower triangular indices.

    • -
    • ValueError – alpha_beta_indices contains lower triangular indices.

    • -
    -
    -
    -
    - -
    -
    -to_t_amplitudes(nocc=None)[source]
    -

    Convert the UCJ operator to t2 (and possibly t1) amplitudes.

    -
    -
    Return type:
    -

    ndarray | tuple[ndarray, ndarray]

    -
    -
    -
    - -
    -
    ffsim.addresses_to_strings(addresses, norb, nelec, concatenate=True, bitstring_type=BitstringType.INT)[source]
    @@ -5295,14 +4826,6 @@
  19. GivensAnsatzOp.to_parameters()
  20. -
  21. GivensAnsatzOperator -
  22. HopGateAnsatzOperator
  23. -
  24. RealUCJOperator -
  25. SingleFactorizedHamiltonian
  26. -
  27. UCJOperator -
  28. addresses_to_strings()
  29. apply_diag_coulomb_evolution()
  30. apply_fsim_gate()
  31. diff --git a/dev/api/ffsim.qiskit.html b/dev/api/ffsim.qiskit.html index 472dae943..e69b99c26 100644 --- a/dev/api/ffsim.qiskit.html +++ b/dev/api/ffsim.qiskit.html @@ -492,80 +492,6 @@
    -
    -
    -class ffsim.qiskit.GivensAnsatzOperatorJW(givens_ansatz_operator, *, label=None)[source]
    -

    Bases: Gate

    -

    Givens rotation ansatz operator under the Jordan-Wigner transformation.

    -
    -

    Warning

    -

    This class is deprecated. Use ffsim.qiskit.GivensAnsatzOpJW instead.

    -
    -

    See ffsim.GivensAnsatzOperator for a description of this gate’s unitary.

    -
    -
    -__init__(givens_ansatz_operator, *, label=None)[source]
    -

    Create a new Givens ansatz operator gate.

    -
    -
    Parameters:
    -
      -
    • givens_ansatz_operator (GivensAnsatzOperator) – The Givens rotation ansatz operator.

    • -
    • label (str | None) – The label of the gate.

    • -
    -
    -
    -
    - -
    -
    -__init_subclass__()
    -

    This method is called when a class is subclassed.

    -

    The default implementation does nothing. It may be -overridden to extend subclasses.

    -
    - -
    -
    -static __new__(*args, **kwargs)
    -
    - -
    - -
    -
    -class ffsim.qiskit.GivensAnsatzOperatorSpinlessJW(givens_ansatz_operator, *, label=None)[source]
    -

    Bases: Gate

    -

    Givens rotation ansatz operator under the Jordan-Wigner transformation, spinless.

    -

    Like GivensAnsatzOperatorJW but only acts on a single spin species.

    -
    -
    -__init__(givens_ansatz_operator, *, label=None)[source]
    -

    Create a new Givens ansatz operator gate.

    -
    -
    Parameters:
    -
      -
    • givens_ansatz_operator (GivensAnsatzOperator) – The Givens rotation ansatz operator.

    • -
    • label (str | None) – The label of the gate.

    • -
    -
    -
    -
    - -
    -
    -__init_subclass__()
    -

    This method is called when a class is subclassed.

    -

    The default implementation does nothing. It may be -overridden to extend subclasses.

    -
    - -
    -
    -static __new__(*args, **kwargs)
    -
    - -
    -
    class ffsim.qiskit.MergeOrbitalRotations(*args, **kwargs)[source]
    @@ -1064,48 +990,6 @@
    -
    -
    -class ffsim.qiskit.UCJOperatorJW(ucj_operator, *, label=None)[source]
    -

    Bases: Gate

    -

    Unitary cluster Jastrow operator under the Jordan-Wigner transformation.

    -
    -

    Warning

    -

    This class is deprecated. Use ffsim.qiskit.UCJOpSpinBalancedJW instead.

    -
    -

    See ffsim.UCJOperator for a description of this gate’s unitary.

    -

    This gate assumes that qubits are ordered such that the first norb qubits -correspond to the alpha orbitals and the last norb qubits correspond to the -beta orbitals.

    -
    -
    -__init__(ucj_operator, *, label=None)[source]
    -

    Create a new unitary cluster Jastrow (UCJ) gate.

    -
    -
    Parameters:
    -
      -
    • ucj_operator (UCJOperator) – The UCJ operator.

    • -
    • label (str | None) – The label of the gate.

    • -
    -
    -
    -
    - -
    -
    -__init_subclass__()
    -

    This method is called when a class is subclassed.

    -

    The default implementation does nothing. It may be -overridden to extend subclasses.

    -
    - -
    -
    -static __new__(*args, **kwargs)
    -
    - -
    -
    ffsim.qiskit.ffsim_vec_to_qiskit_vec(vec, norb, nelec)[source]
    @@ -1183,7 +1067,7 @@ :rtype: Iterator[BasePass]

    -
  32. GivensAnsatzOperatorJW -
  33. -
  34. GivensAnsatzOperatorSpinlessJW -
  35. MergeOrbitalRotations @@ -1374,12 +1246,6 @@
  36. UCJOpSpinlessJW.__init__()
  37. -
  38. UCJOperatorJW -
  39. ffsim_vec_to_qiskit_vec()
  40. final_state_vector()
  41. jordan_wigner()
  42. diff --git a/dev/api/ffsim.random.html b/dev/api/ffsim.random.html index 31e077202..9aa1121dc 100644 --- a/dev/api/ffsim.random.html +++ b/dev/api/ffsim.random.html @@ -740,30 +740,6 @@
    -
    -
    -ffsim.random.random_ucj_operator(norb, *, n_reps=1, with_final_orbital_rotation=False, seed=None)[source]
    -

    Sample a random unitary cluster Jastrow (UCJ) operator.

    -
    -
    Parameters:
    -
      -
    • norb (int) – The number of orbitals.

    • -
    • n_reps (int) – The number of ansatz repetitions.

    • -
    • with_final_orbital_rotation (bool) – Whether to include a final orbital rotation -in the operator.

    • -
    • seed – A seed to initialize the pseudorandom number generator. -Should be a valid input to np.random.default_rng.

    • -
    -
    -
    Return type:
    -

    UCJOperator

    -
    -
    Returns:
    -

    The sampled UCJ operator.

    -
    -
    -
    -
    ffsim.random.random_unitary(dim, *, seed=None, dtype=<class 'complex'>)[source]
    @@ -866,7 +842,6 @@
  43. random_ucj_op_spin_balanced()
  44. random_ucj_op_spin_unbalanced()
  45. random_ucj_op_spinless()
  46. -
  47. random_ucj_operator()
  48. random_unitary()
  49. diff --git a/dev/api/index.html b/dev/api/index.html index 3b5e87e3c..241a7083e 100644 --- a/dev/api/index.html +++ b/dev/api/index.html @@ -285,13 +285,11 @@

    API referenceFermionAction
  50. FermionOperator
  51. GivensAnsatzOp
  52. -
  53. GivensAnsatzOperator
  54. HopGateAnsatzOperator
  55. MolecularData
  56. MolecularHamiltonian
  57. NumNumAnsatzOpSpinBalanced
  58. ProductStateSum
  59. -
  60. RealUCJOperator
  61. SingleFactorizedHamiltonian
  62. Spin
  63. StateVector
  64. @@ -305,7 +303,6 @@

    API referenceUCJOpSpinBalanced
  65. UCJOpSpinUnbalanced
  66. UCJOpSpinless
  67. -
  68. UCJOperator
  69. addresses_to_strings()
  70. apply_diag_coulomb_evolution()
  71. apply_fsim_gate()
  72. @@ -401,8 +398,6 @@

    API referenceFfsimSampler
  73. GivensAnsatzOpJW
  74. GivensAnsatzOpSpinlessJW
  75. -
  76. GivensAnsatzOperatorJW
  77. -
  78. GivensAnsatzOperatorSpinlessJW
  79. MergeOrbitalRotations
  80. NumNumAnsatzOpSpinBalancedJW
  81. NumOpSumEvolutionJW
  82. @@ -419,7 +414,6 @@

    API referenceUCJOpSpinBalancedJW
  83. UCJOpSpinUnbalancedJW
  84. UCJOpSpinlessJW
  85. -
  86. UCJOperatorJW
  87. ffsim_vec_to_qiskit_vec()
  88. final_state_vector()
  89. jordan_wigner()
  90. @@ -446,7 +440,6 @@

    API referencerandom_ucj_op_spin_balanced()
  91. random_ucj_op_spin_unbalanced()
  92. random_ucj_op_spinless()
  93. -
  94. random_ucj_operator()
  95. random_unitary()
  96. diff --git a/dev/explanations/hamiltonians.html b/dev/explanations/hamiltonians.html index 91234a1fc..8e397a8d8 100644 --- a/dev/explanations/hamiltonians.html +++ b/dev/explanations/hamiltonians.html @@ -377,7 +377,7 @@

    Operator action via SciPy LinearOperators
    -np.float64(-99.55717072551545)
    +np.float64(-99.55717072551536)
     

    Time evolution by the Hamiltonian can be computed using expm_multiply:

    @@ -395,7 +395,7 @@

    Operator action via SciPy LinearOperators
    -/tmp/ipykernel_4222/2190712273.py:2: UserWarning: Trace of LinearOperator not available, it will be estimated. Provide `traceA` to ensure performance.
    +/tmp/ipykernel_4234/2190712273.py:2: UserWarning: Trace of LinearOperator not available, it will be estimated. Provide `traceA` to ensure performance.
       evolved_vec = scipy.sparse.linalg.expm_multiply(-1j * time * linop, vec)
     

    diff --git a/dev/explanations/hamiltonians.ipynb b/dev/explanations/hamiltonians.ipynb index fdab3b0e5..a64798d6c 100644 --- a/dev/explanations/hamiltonians.ipynb +++ b/dev/explanations/hamiltonians.ipynb @@ -33,10 +33,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:02.728608Z", - "iopub.status.busy": "2024-10-30T01:18:02.728416Z", - "iopub.status.idle": "2024-10-30T01:18:03.436749Z", - "shell.execute_reply": "2024-10-30T01:18:03.436173Z" + "iopub.execute_input": "2024-10-30T02:09:49.027067Z", + "iopub.status.busy": "2024-10-30T02:09:49.026879Z", + "iopub.status.idle": "2024-10-30T02:09:49.763447Z", + "shell.execute_reply": "2024-10-30T02:09:49.762853Z" } }, "outputs": [], @@ -68,10 +68,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:03.439342Z", - "iopub.status.busy": "2024-10-30T01:18:03.438783Z", - "iopub.status.idle": "2024-10-30T01:18:03.441963Z", - "shell.execute_reply": "2024-10-30T01:18:03.441460Z" + "iopub.execute_input": "2024-10-30T02:09:49.765836Z", + "iopub.status.busy": "2024-10-30T02:09:49.765405Z", + "iopub.status.idle": "2024-10-30T02:09:49.768532Z", + "shell.execute_reply": "2024-10-30T02:09:49.768045Z" } }, "outputs": [], @@ -99,10 +99,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:03.443901Z", - "iopub.status.busy": "2024-10-30T01:18:03.443455Z", - "iopub.status.idle": "2024-10-30T01:18:03.446765Z", - "shell.execute_reply": "2024-10-30T01:18:03.446180Z" + "iopub.execute_input": "2024-10-30T02:09:49.770582Z", + "iopub.status.busy": "2024-10-30T02:09:49.770211Z", + "iopub.status.idle": "2024-10-30T02:09:49.773313Z", + "shell.execute_reply": "2024-10-30T02:09:49.772854Z" } }, "outputs": [], @@ -127,10 +127,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:03.449121Z", - "iopub.status.busy": "2024-10-30T01:18:03.448501Z", - "iopub.status.idle": "2024-10-30T01:18:03.453127Z", - "shell.execute_reply": "2024-10-30T01:18:03.452566Z" + "iopub.execute_input": "2024-10-30T02:09:49.775342Z", + "iopub.status.busy": "2024-10-30T02:09:49.774996Z", + "iopub.status.idle": "2024-10-30T02:09:49.779913Z", + "shell.execute_reply": "2024-10-30T02:09:49.779377Z" } }, "outputs": [], @@ -152,17 +152,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:03.456089Z", - "iopub.status.busy": "2024-10-30T01:18:03.455300Z", - "iopub.status.idle": "2024-10-30T01:18:03.483908Z", - "shell.execute_reply": "2024-10-30T01:18:03.483194Z" + "iopub.execute_input": "2024-10-30T02:09:49.782880Z", + "iopub.status.busy": "2024-10-30T02:09:49.782061Z", + "iopub.status.idle": "2024-10-30T02:09:49.811144Z", + "shell.execute_reply": "2024-10-30T02:09:49.810476Z" } }, "outputs": [ { "data": { "text/plain": [ - "np.float64(-99.55717072551545)" + "np.float64(-99.55717072551536)" ] }, "execution_count": 5, @@ -191,10 +191,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:03.518338Z", - "iopub.status.busy": "2024-10-30T01:18:03.517862Z", - "iopub.status.idle": "2024-10-30T01:18:04.161797Z", - "shell.execute_reply": "2024-10-30T01:18:04.161210Z" + "iopub.execute_input": "2024-10-30T02:09:49.843813Z", + "iopub.status.busy": "2024-10-30T02:09:49.843363Z", + "iopub.status.idle": "2024-10-30T02:09:50.493704Z", + "shell.execute_reply": "2024-10-30T02:09:50.493075Z" } }, "outputs": [ @@ -202,7 +202,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_4222/2190712273.py:2: UserWarning: Trace of LinearOperator not available, it will be estimated. Provide `traceA` to ensure performance.\n", + "/tmp/ipykernel_4234/2190712273.py:2: UserWarning: Trace of LinearOperator not available, it will be estimated. Provide `traceA` to ensure performance.\n", " evolved_vec = scipy.sparse.linalg.expm_multiply(-1j * time * linop, vec)\n" ] } @@ -224,10 +224,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:04.165049Z", - "iopub.status.busy": "2024-10-30T01:18:04.164211Z", - "iopub.status.idle": "2024-10-30T01:18:04.752315Z", - "shell.execute_reply": "2024-10-30T01:18:04.751757Z" + "iopub.execute_input": "2024-10-30T02:09:50.497160Z", + "iopub.status.busy": "2024-10-30T02:09:50.496198Z", + "iopub.status.idle": "2024-10-30T02:09:51.112728Z", + "shell.execute_reply": "2024-10-30T02:09:51.112100Z" } }, "outputs": [], diff --git a/dev/explanations/orbital-rotation.ipynb b/dev/explanations/orbital-rotation.ipynb index 068c624ac..53155bfc3 100644 --- a/dev/explanations/orbital-rotation.ipynb +++ b/dev/explanations/orbital-rotation.ipynb @@ -62,10 +62,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:07.610992Z", - "iopub.status.busy": "2024-10-30T01:18:07.610806Z", - "iopub.status.idle": "2024-10-30T01:18:08.313302Z", - "shell.execute_reply": "2024-10-30T01:18:08.312639Z" + "iopub.execute_input": "2024-10-30T02:09:54.371994Z", + "iopub.status.busy": "2024-10-30T02:09:54.371552Z", + "iopub.status.idle": "2024-10-30T02:09:55.112686Z", + "shell.execute_reply": "2024-10-30T02:09:55.112160Z" } }, "outputs": [], diff --git a/dev/explanations/qiskit-gate-decompositions.ipynb b/dev/explanations/qiskit-gate-decompositions.ipynb index 37a68f9f6..cbe4ca86c 100644 --- a/dev/explanations/qiskit-gate-decompositions.ipynb +++ b/dev/explanations/qiskit-gate-decompositions.ipynb @@ -16,10 +16,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:09.747117Z", - "iopub.status.busy": "2024-10-30T01:18:09.746661Z", - "iopub.status.idle": "2024-10-30T01:18:12.572783Z", - "shell.execute_reply": "2024-10-30T01:18:12.572145Z" + "iopub.execute_input": "2024-10-30T02:09:56.540941Z", + "iopub.status.busy": "2024-10-30T02:09:56.540747Z", + "iopub.status.idle": "2024-10-30T02:09:58.179701Z", + "shell.execute_reply": "2024-10-30T02:09:58.179102Z" } }, "outputs": [ @@ -81,10 +81,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:12.575149Z", - "iopub.status.busy": "2024-10-30T01:18:12.574683Z", - "iopub.status.idle": "2024-10-30T01:18:12.763089Z", - "shell.execute_reply": "2024-10-30T01:18:12.762506Z" + "iopub.execute_input": "2024-10-30T02:09:58.182043Z", + "iopub.status.busy": "2024-10-30T02:09:58.181548Z", + "iopub.status.idle": "2024-10-30T02:09:58.388591Z", + "shell.execute_reply": "2024-10-30T02:09:58.387988Z" } }, "outputs": [ @@ -119,10 +119,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:12.765303Z", - "iopub.status.busy": "2024-10-30T01:18:12.764966Z", - "iopub.status.idle": "2024-10-30T01:18:12.872452Z", - "shell.execute_reply": "2024-10-30T01:18:12.871846Z" + "iopub.execute_input": "2024-10-30T02:09:58.390983Z", + "iopub.status.busy": "2024-10-30T02:09:58.390694Z", + "iopub.status.idle": "2024-10-30T02:09:58.500594Z", + "shell.execute_reply": "2024-10-30T02:09:58.500004Z" } }, "outputs": [ @@ -156,10 +156,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:12.874397Z", - "iopub.status.busy": "2024-10-30T01:18:12.874061Z", - "iopub.status.idle": "2024-10-30T01:18:12.982494Z", - "shell.execute_reply": "2024-10-30T01:18:12.981922Z" + "iopub.execute_input": "2024-10-30T02:09:58.502908Z", + "iopub.status.busy": "2024-10-30T02:09:58.502544Z", + "iopub.status.idle": "2024-10-30T02:09:58.613368Z", + "shell.execute_reply": "2024-10-30T02:09:58.612753Z" } }, "outputs": [ @@ -196,10 +196,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:12.984356Z", - "iopub.status.busy": "2024-10-30T01:18:12.984142Z", - "iopub.status.idle": "2024-10-30T01:18:13.165649Z", - "shell.execute_reply": "2024-10-30T01:18:13.165155Z" + "iopub.execute_input": "2024-10-30T02:09:58.615746Z", + "iopub.status.busy": "2024-10-30T02:09:58.615356Z", + "iopub.status.idle": "2024-10-30T02:09:58.800638Z", + "shell.execute_reply": "2024-10-30T02:09:58.800009Z" } }, "outputs": [ @@ -250,10 +250,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:13.167631Z", - "iopub.status.busy": "2024-10-30T01:18:13.167340Z", - "iopub.status.idle": "2024-10-30T01:18:13.384578Z", - "shell.execute_reply": "2024-10-30T01:18:13.384059Z" + "iopub.execute_input": "2024-10-30T02:09:58.803009Z", + "iopub.status.busy": "2024-10-30T02:09:58.802627Z", + "iopub.status.idle": "2024-10-30T02:09:59.025496Z", + "shell.execute_reply": "2024-10-30T02:09:59.024902Z" } }, "outputs": [ @@ -292,10 +292,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:13.386502Z", - "iopub.status.busy": "2024-10-30T01:18:13.386129Z", - "iopub.status.idle": "2024-10-30T01:18:13.518827Z", - "shell.execute_reply": "2024-10-30T01:18:13.518261Z" + "iopub.execute_input": "2024-10-30T02:09:59.027842Z", + "iopub.status.busy": "2024-10-30T02:09:59.027426Z", + "iopub.status.idle": "2024-10-30T02:09:59.164215Z", + "shell.execute_reply": "2024-10-30T02:09:59.163691Z" } }, "outputs": [ @@ -334,10 +334,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:13.520818Z", - "iopub.status.busy": "2024-10-30T01:18:13.520487Z", - "iopub.status.idle": "2024-10-30T01:18:14.031045Z", - "shell.execute_reply": "2024-10-30T01:18:14.030435Z" + "iopub.execute_input": "2024-10-30T02:09:59.166452Z", + "iopub.status.busy": "2024-10-30T02:09:59.165894Z", + "iopub.status.idle": "2024-10-30T02:09:59.695006Z", + "shell.execute_reply": "2024-10-30T02:09:59.694428Z" } }, "outputs": [ @@ -378,10 +378,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:14.033274Z", - "iopub.status.busy": "2024-10-30T01:18:14.032831Z", - "iopub.status.idle": "2024-10-30T01:18:14.214030Z", - "shell.execute_reply": "2024-10-30T01:18:14.213518Z" + "iopub.execute_input": "2024-10-30T02:09:59.697356Z", + "iopub.status.busy": "2024-10-30T02:09:59.696979Z", + "iopub.status.idle": "2024-10-30T02:09:59.886497Z", + "shell.execute_reply": "2024-10-30T02:09:59.885807Z" } }, "outputs": [ @@ -430,10 +430,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:14.216075Z", - "iopub.status.busy": "2024-10-30T01:18:14.215684Z", - "iopub.status.idle": "2024-10-30T01:18:14.382744Z", - "shell.execute_reply": "2024-10-30T01:18:14.382266Z" + "iopub.execute_input": "2024-10-30T02:09:59.889396Z", + "iopub.status.busy": "2024-10-30T02:09:59.888809Z", + "iopub.status.idle": "2024-10-30T02:10:00.090115Z", + "shell.execute_reply": "2024-10-30T02:10:00.089589Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:14.384733Z", - "iopub.status.busy": "2024-10-30T01:18:14.384351Z", - "iopub.status.idle": "2024-10-30T01:18:14.513562Z", - "shell.execute_reply": "2024-10-30T01:18:14.513039Z" + "iopub.execute_input": "2024-10-30T02:10:00.092257Z", + "iopub.status.busy": "2024-10-30T02:10:00.091864Z", + "iopub.status.idle": "2024-10-30T02:10:00.223234Z", + "shell.execute_reply": "2024-10-30T02:10:00.222527Z" } }, "outputs": [ @@ -513,10 +513,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:14.515595Z", - "iopub.status.busy": "2024-10-30T01:18:14.515232Z", - "iopub.status.idle": "2024-10-30T01:18:14.692478Z", - "shell.execute_reply": "2024-10-30T01:18:14.691934Z" + "iopub.execute_input": "2024-10-30T02:10:00.225524Z", + "iopub.status.busy": "2024-10-30T02:10:00.225171Z", + "iopub.status.idle": "2024-10-30T02:10:00.404126Z", + "shell.execute_reply": "2024-10-30T02:10:00.403500Z" } }, "outputs": [ @@ -553,10 +553,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:14.694463Z", - "iopub.status.busy": "2024-10-30T01:18:14.694089Z", - "iopub.status.idle": "2024-10-30T01:18:14.851809Z", - "shell.execute_reply": "2024-10-30T01:18:14.851280Z" + "iopub.execute_input": "2024-10-30T02:10:00.406362Z", + "iopub.status.busy": "2024-10-30T02:10:00.405851Z", + "iopub.status.idle": "2024-10-30T02:10:00.565779Z", + "shell.execute_reply": "2024-10-30T02:10:00.565154Z" } }, "outputs": [ @@ -593,10 +593,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:14.853676Z", - "iopub.status.busy": "2024-10-30T01:18:14.853481Z", - "iopub.status.idle": "2024-10-30T01:18:14.983082Z", - "shell.execute_reply": "2024-10-30T01:18:14.982518Z" + "iopub.execute_input": "2024-10-30T02:10:00.568093Z", + "iopub.status.busy": "2024-10-30T02:10:00.567623Z", + "iopub.status.idle": "2024-10-30T02:10:00.699470Z", + "shell.execute_reply": "2024-10-30T02:10:00.698933Z" } }, "outputs": [ @@ -630,10 +630,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:14.985145Z", - "iopub.status.busy": "2024-10-30T01:18:14.984776Z", - "iopub.status.idle": "2024-10-30T01:18:15.142916Z", - "shell.execute_reply": "2024-10-30T01:18:15.142318Z" + "iopub.execute_input": "2024-10-30T02:10:00.701705Z", + "iopub.status.busy": "2024-10-30T02:10:00.701337Z", + "iopub.status.idle": "2024-10-30T02:10:00.860858Z", + "shell.execute_reply": "2024-10-30T02:10:00.860218Z" } }, "outputs": [ @@ -677,10 +677,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:15.145121Z", - "iopub.status.busy": "2024-10-30T01:18:15.144676Z", - "iopub.status.idle": "2024-10-30T01:18:15.322744Z", - "shell.execute_reply": "2024-10-30T01:18:15.322201Z" + "iopub.execute_input": "2024-10-30T02:10:00.863094Z", + "iopub.status.busy": "2024-10-30T02:10:00.862748Z", + "iopub.status.idle": "2024-10-30T02:10:01.058547Z", + "shell.execute_reply": "2024-10-30T02:10:01.058004Z" } }, "outputs": [ @@ -736,16 +736,16 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:15.325193Z", - "iopub.status.busy": "2024-10-30T01:18:15.324673Z", - "iopub.status.idle": "2024-10-30T01:18:15.789795Z", - "shell.execute_reply": "2024-10-30T01:18:15.789259Z" + "iopub.execute_input": "2024-10-30T02:10:01.060929Z", + "iopub.status.busy": "2024-10-30T02:10:01.060430Z", + "iopub.status.idle": "2024-10-30T02:10:01.510763Z", + "shell.execute_reply": "2024-10-30T02:10:01.510137Z" } }, "outputs": [ { "data": { - "image/png": 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", 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z5s189913xMXFkZWVxaeffkrv3r2JjY1l69atnDhxgqioKLZt24bNZquVYxYRERERERHPpmSXiNClSxcOHTpUtrxjxw7at29f5Wt++OEHBg4cCED//v3ZunVrhTpr1qyhX79+vP/++3h5eQFQWlpKixYt8PX1JS0tjauuugqAqKgofvzxRwDatGnD7t27a+TYREREREREpG5RsktEiIuLIzIysmw5OTmZ8PDwKl+TmZlJQIB9Rn9/f38yMzPLrQ8JCSExMZG1a9fy5ptvcurUKZYuXUpUVBSBgYGYzWY6dOjAN998g81m49tvv+XMmTMAhIWFkZSUVKPHKCIiIiIiInWDJqgXqcMWLlzI+vXr6du3L9HR0ZXW+fnnn3n00UfLld14440EBgaSlZUFQHZ2Nm3bti1Xx9vbu+zvfv36kZqayt13381dd93F6NGj2bVrF8OHD2ft2rUMGTKEJk2a0LRp05o9QBEREREREalzlOwSqcMmTJhQNln8+dq1a8enn34KQKdOnYiNja1Q58cff+SDDz7glltuYePGjdxxxx3l1ufk5ODn54fNZmPLli1MmjQJAKPRiL+/Pz4+PphMJubNm4fNZmPcuHH06tULgPT0dPr06VPDRysiIiIiIiJ1gYYxikgF3bp1IzExsco61157LYWFhfTr149u3brRrFkzjh49WvZUxe+++47u3bvTu3dvhg4dSnx8PBaLheuvv55WrVrRrl07Dhw4gMViYdCgQdx+++34+voCkJKSQpcuXWr9OEVERERERMTzqGeXSB1lsViwWCyVrjMajXTt2pXk5OQqn8g4d+7ccsvNmzfnmWeeAWDo0KEMHTq03PrRo0eXW27dunWFXmN79uzhmmuuwWAwXOKRiIiIiIiIiJyjZJeIVKqy4Y2OEBUVRVRUlFP2LSIiIiIiIu5PwxhFRERERERERMRjKNklIiIiIiIiIiIeQ8MYxeXkFENKlnP2HRkAfl7O2beIiIiIiIiIVJ+SXeJyUrJg4mbn7HtBH4gOds6+RURERERERKT6NIxRREREREREREQ8hpJdIiIiIiIiIiLiMZTsEhERERERERERj6Fkl4iIiIiIiIiIeAxNUC8eIXGqhdy9cRjMXhhMZnxadqTF2FkEdBvk7NBERERERERExIHUs0s8Rouxs4hZlkPXRcdo0KEXqbNvpTT3jLPDEhEREREREREHUrJLPI7Ry5vGQyZgLcih4EiKs8MREREREREREQdSsks8jrUwn5PrFmD0DcCnRTtnhyMiIiIiIiIiDuQ2ya6ioiKmT59OaGgo9evXZ+DAgcTHx2MwGFixYoWzwxMXcHjpdBLuDmTXg+Hkpe0gctoXmHwDnB2WUxSVnvvbZnNeHCI1wWaDo3nwSy4Ull68voirO10IB3Igp9jZkYiIuygohYM5cDRfbTtxf1YbHP61bVdsdXY04qncYoJ6m83G6NGjiY+PZ8aMGURGRrJ06VJGjhwJQExMjMNiCb+lF1ETbyKocziFGdks7/Gow/YtVWtx90s0H/m0s8NwqpxieDsZ/pt+rmzkVzCmLYwOB6PBaaGJXDabDT5Jh6WpcCDXXtbADLe2hgntIaCec+MTuVw/nrC/R/940r5sNMDAEJjYHiLr5r0ZEbmI04WwMAk+PwB5v97wifCDsZEwPBQMatuJGym1wYf77D9H8u1lDb3gtnB4oB3Ud4vshLgLt7ic5s+fz6pVq0hISKBz584AWCwWIiIiCAoKonXr1g6LpfBMLnvf/hKfJg3pPOl3DtuvyMVkF8PETbAvG86/4XcoD17eBT+fhr/EKOEl7sFmg1k74LMDcP4lm1sCS/fB5uOwsC80VMJL3MSXv8D0beWvZ6sNvj4Mm47BvF5wVZDTwhMRF3SqAB7YZO8Bc37bbn8OvJQAKVnwx85KeIl7KLXBlC3w1ZHyn4VniuHdZPj+OLzZB3zdIkMh7sAthjHOmTOHsWPHliW6AEwmExEREWW9up5++mkiIiIwGAz89NNPtRbLkY07SVuxmdxfTtTaPkSuxBt7Kia64Nzy/36BdYcdHZXIlYk9ak90QeXX9IEceO1nR0clcmVOF8KM7fZr97ejNazYh57/aav9i4CIyFmv/ARH8i7ctvtgH/ygryTiJlYesCe6oPJreu8ZeCvR0VGJJ3P5ZFdycjJpaWmMGjWqwrqDBw+WJbtuvfVWNm7cSFhYmKNDFHG63BJYebDiB8f5jMCyfY6KSKR6Pkqr+gPKCqz+BbKKHBWRyJVbcQBKqniDtmKfhyfuuMNCEhEXd6rAnhioajojowGWpTksJJFq+SitfI+u37Jhn4pF87NKTXH5ToKHDh0CoGnTpuXK9+7dS3p6elmyq2/fvle8D39/f4qKKn5jamtuxLMBV75dKW/AAAupJacvWs+3Qy/azoy9rG13mHV59S9kwAALeYlxNbItR6ofeS2Rf91UZR0rsONEMd7efo4Jqo7o8lEBAN7ePk6OxLN0fu80Rm/fKusUWSGsxyDy9lR97cul0/VcO1o/8xEB1/wOg8l0wTo2ayljnvkbx5e96MDIPJ+uaXFXftE3EDH18yrrWG2wIfkk3te3dFBUdYPeN2qBycxVH+ZetFpOCTTpEEPhQXXfFzur9cqfYODyPbuCg4MBSElJKSuz2Ww899xzWK1Wh05OL+KybJf6JqAxMuIebJd6TeuRVOIOrFYu6f1X17OInKW2nXiSy/l802eh1BCX79kVFRVFZGQkU6ZMwcvLCz8/P+bNm8f27dvx9fWlQ4cO1d5HdnZ2peXH4veyesS0am9f7DZsiKVZj44XrZdwCiZudkBAldiwIZboYOfsuzoKSuCGtfbhjBdiBK5pXo+thYUOi6su6P7rTddCndca9fvvIe6E/a71hfiY4JetX9PA5T/J3Ieu59qxNBX+ubvqOgajiU/+OY0eS9TuqEm6psVdZRbBjWuqHgJtBG7s2IQEXd81Su8bteP+jfBzZtVDcwPrwankBLxcvkuOOMrmzZuveBSfy19GZrOZ5cuXExISwrhx45g8eTLDhg2jf//+dO3aFaPRsYdgMBoxeXthMJvBYMDk7YWxnr5piXP5mGFkWNXj4K3AnRGOikikeu5sU3WiywAMb40SXeIWfhcK3sYLv0cbgdYN4NrGjoxKRFxZYD0Y1uribbs71LYTN3FXm6oTXQCjw1GiS2qMW1xK3bp1Iy4ujry8PBITE3nkkUfYvn27U4Ywtr39eu7d/wED3noKv1ZNuHf/B9y26TWHxyHyWw93gKsaVSw/+598TBvo39yhIYlcsd5N4b5I+9+VfVB1bAiPRzk0JJErFlAP/tYdTIaK17MRaOAFf78WDFV9qxWROuePXaCtf8WE19n3kUkd4Go3HJEgddMNLeHW1va/z/8sPHt9X9sYxrdzdFTiydzynnh+fj6JiYlMnjy5rOzJJ5/k008/5ejRowwePJjg4GB2777ImIErkPJRLCkfxdb4duXiDsx/gvy0BAKuHkbI6Cll5SfXv83JtQswBzYlYvISTL7+AOTtS2DP09dyzafFAOQm/sCh96Zgs1ppNW4ODdr3cMpx1BYfM7zRGz7cBx+nwTH73JpEBdoTXTe01BcpcS9PREGnQHg/FXb9+myLJj72u35j2kB9t/wEk7qqX3N4px8sToF1h+1lPiZ7r69720LLBs6NT0Rcj78XLOgLH+yDj/dDxq+j6roFwT1twRLi1PBELovBAFO7QXQQLN0HSVn28hBfew/FOyPUq0tqllt+Vdi5cyelpaXlena99tprvPaaelh5qtzkLRiMZjr89VtS/3Y7xZnH8Apshq20hFNfL6LD3zZxZusqTq5bQLMRfwDgxJfz8G1zNWB/qMGxFa8Q+edVGL28nXkotcrHBPe3s/eIyS2x9yLwdcv/5SL2RtHgFvafs/Nn/G+I/VHrIu4oKhD+2h3W/Xo9xw4Dsxr2IlIFPy94sANMaA89VtrL3tLD4sVNGQzwu9b2n7NtuxWDdENeaodbNrF69uyJzWaje/fuzg5FHCQ36Qf8uw4EwL9Lf/JStgJQknWSek1aYzAaqR92FbmJcQAUHE7GHNAEU317L6/Co6lYi/JJnTmctFfuobTg4o++dWdGg/1uoBJd4mmU6BJPokSXiFwqff6Jp1KiS2qLmlniFkpzMzH5BgBgrO9PaW4mAOaAJhQeScFaVEDO7o2U5p4B4PjKf9H05sfLXl+SeZzCI6m0nfY5/t0Gc3LtWw4/BhERERERERGpfUp2iVswNQikNM8+sNuan42pQSAABpOJZiOfIfmFG8nfvwNzYDOKTtknQ/Fq1Py81zekQfueGL288e98PQWHEh1+DCIiIiIiIiJS+5TsErfQoH0PsndtACB790Z8I68pW9eo1210mBVLg/Y9aXjNMAoO/kxeWgLJM24kLy2BA/OfxKdFe4pPH8Fms5GfvgvvpuFOOhIRERERERERqU2a0UfcQoN213Lqq3dJ/FM/AmJuoDjjCFnb1xI84F4O/OdRCg4l4hPaidAJr2IwmQiIHgxA0vTBtJ5kf3BBUP97SJpyPcZ6vkQ8/YEzD0dEREREREREaomSXeI2Wj88t9yyb5voX8vfuOBr2r+0vuzv4AH3Ejzg3lqJTURERERERERcg4YxioiIiIiIiIiIx1CyS0REREREREREPIaSXSIiIiIiIiIi4jGU7BIREREREREREY+hCerF5UQGwII+ztu3SE2JjY1l/PjxhIWFERYWxqJFi8qtX7JkCSEhIQwePJh+/fqxfft29u7dS6tWrcrVmzFjBitWrKBhw4ZMmjSJu+++mxMnTjBp0iQyMzMZM2YMkyZNYvz48bz99tsYDAZHHqbT1dR5PismJoannnqKsWPHMn36dCZNmkRoaKgjDkVERERERGqAkl3icvy8IDrY2VGI1IwJEyYwbdo0nnzySeLi4ujVq1fZupUrV7Js2TIAPv74Y55//vkLbuff//43ffv2LVt+8cUXefXVVwkLCysr69mzJxs2bGDgwIG1cCSurabO8+rVqwkKCipbvueee3jrrbd48cUXay94ERERERGpURrGKCLiAF26dOHQoUNlyxkZGfj4+JT1wmrevHmVr588eTI333wz+/fvByApKYnnn3+eoUOHkpiYCMCAAQNYtWpV7RyAm6jueX7//fcZM2ZM2XLHjh3Ztm1b7QQrIiIiIiK1QskuEREHiIuLIzIysmw5JSWlXK+sqvz+979ny5YtzJgxg+eeew6ATZs2MXPmTObOncvUqVMBCAsLIykpqeaDdyPVOc+bN2+me/fumM3lOz0XFRXVaIwiIiIiIlK7lOwSEalFCxcuxGKx0LJlS6Kjo69oG40aNQLg2muv5dSpUwB07tyZtm3b0q5dO86cOVNT4bqtmjjPb775JhMnTqzZwERERERExOE0Z5eISC06O5fUb0VGRpKenn5J28jOzsbf3599+/bRoEEDAMLDwzl58iSlpaX4+voCkJ6eTrt27WoueDdSE+d537593H777Rw6dAij0Ui/fv0ICwujXr16NR2uiIiIiIjUIiW7REScICgoiLy8vLLlsWPH8vXXX5OamsqMGTMYNGgQTz31FK+88grPPfccO3bswGg0MnfuXACmTZvGbbfdRklJCa+99hoAGzZsYNiwYU45Hld1Oed506ZNALz77ruYzWbCwsLYu3fvFfcUExERERER51CyS0SkllgsFiwWywXXDx8+nPXr1zN48GDee++9CutfeeUVAN54440K67p27crGjRvLlf3www9MmjSpekG7oZo6z2fdf//9ZX+///77dfKcioiIiIi4MyW7RESc5L777qvR7b3zzjs1uj1PUZ3z/NJLL9VgJCIiIiIi4giaoF5ERERERERERDyGkl0iIiIiIiIiIuIxlOwSERERERERERGPoWSXiIiIiIiIiIh4DE1QLy4npxhSspyz78gA8PNyzr5FREREREREpPqU7BKXk5IFEzc7Z98L+kB0sHP2LSIiIiIiIiLVp2GMIiIiIiIiIiLiMZTsEhERERERERERj6Fkl4iIiIiIiIiIeAwlu0RERERERERExGNognrxCIlTLeTujcNg9sJgMuPTsiMtxs4ioNsgZ4cmIiIiIiIiIg6knl3iMVqMnUXMshy6LjpGgw69SJ19K6W5Z5wdloiIiIiIiIg4kJJd4nGMXt40HjIBa0EOBUdSnB2OiIiIiIiIiDiQkl3icayF+ZxctwCjbwA+Ldo5OxwRERERERERcSC3SXYVFRUxffp0QkNDqV+/PgMHDiQ+Ph6DwcCKFSucHZ64gMNLp5NwdyC7HgwnL20HkdO+wOQb4OywxIPZbJX/LbXHqvMsIuJSSqyQWQgFJc6OREREfiuvBDKLoLQOtqHdYoJ6m83G6NGjiY+PZ8aMGURGRrJ06VJGjhwJQExMjMNiMdYzc93siYT0vQqf4ADyjp1mz9ur2fv2aofFIJVrcfdLNB/5tLPDkDqg1Ab/3Q8fpp0rG/U13NkGRoWB2W1uI7g2mw3WHYalqefKbl4Ho8LhnjZQ3y0+wUREPNPJAlicAisOQG4JGIDeTeHeSOje2NnRiYjUbd8chfdSYHuGfblRPXsbemxb8PNyamgO4xZfFebPn8+qVatISEigc+fOAFgsFiIiIggKCqJ169YOi8VoMpF/PJO1d71EdvoxgjqFMeSDaRScyGT/yjiHxSEizlFqgylb4Ksj9ob9WQdz4eVd8MNx+Pu1SnjVhNd+hiWp5bsgnyiA/+yFb47Af/pAA7f4FBMR8SyH82DCJnvC62xnARsQdwK+Ow7To2G445rnIiJynreT4I29YDzvy8rpIliYBF8dhrf6QmA958XnKG7xdWzOnDmMHTu2LNEFYDKZiIiIICYmhlOnTnHTTTfRoUMHrrrqKm677TZOnDhRK7GU5Bey/e8fkr3/KNhsZOzez8G1W2jaI6pW9iciruXT/fZEF5xr4J//98Zj5Xt8yZXZfMye6AKwVrJ+7xl4/WeHhiQiIr/68zY4VVD+cxDsQ81twMwEOJTrhMBEROq4nRn2RBdUnP7DBqTnwD92OTwsp3D5ZFdycjJpaWmMGjWqwrqDBw8SExODwWDg2WefJTExkV27dtG2bVuef/55h8RnMJto1jOK03vSHbI/EXEemw0+2Fe+R9dvGYBl+zS3VHV9lFb+btRv2YCVB+1DZ0RExHFSsiAho/IbEWfZgE/VNBYRcbiP0qpO8lixTxNyqsBRETmPyw8AOXToEABNmzYtV753717S09OJiYkhKCgIi8VStu66665j3rx5l7wPf39/ioqKKpS3NTfi2YC+Vb72utkTKM7JJ/Xjby55f3XVgAEWUktOX7Seb4detJ0Ze1nb7jDr8upfyIABFvISNRxVKmfyD6bT24errGMDjuSDf4sISjKqrisX1nlJBkafBlXWKSiFVtcMJG/vZgdF5fm6fGRv+Xh7+zg5Es+m8+w4Otc1L2joQ7R88LUq69iAN1Z9z9Mx/R0TVB2h69kxdJ4dQ+e5dnR8cz9eQSFV1im1QYeBt5G99X8OiurKWa1V3Vqpmsv37AoODgYgJSWlrMxms/Hcc89htVorTE5vtVqZN28ew4cPr/XYrp0xjibXdGDdPbOwFqt7gYjHM1TVp6tC5VoLo0641HN9Wf8mIiJSbZf4vmvQ56CIiOOpDV3G5Xt2RUVFERkZyZQpU/Dy8sLPz4958+axfft2fH196dChQ7n6TzzxBH5+fjz++OOXvI/s7OxKy4/F72X1iGmVruvx4v2E9L2KL0e/QGFG5a+X8jZsiKVZj44XrZdwCiY6qaPGhg2xRAc7Z9/i+qw2GLEejuZXnKfkfE184IfD+zB5/mdIrXnkO9h6suphMvWMkB7/FQF1YIJNR+n+uf13YWGhcwPxcDrPjqNzXfP2ZMK9G6uuYwAm3tiTPz6t816TdD07hs6zY+g8145nf4TYo1VPqWIEfl7/CU3rOyysK7Z582b69q16tN2FuHzPLrPZzPLlywkJCWHcuHFMnjyZYcOG0b9/f7p27YrReO4Qnn76aZKTk1m2bFm58prW46UHCOnX1Z7oOpVVa/sREddiNMCdbapOdAGMDkeJrmq6M6LqRJcBGNYKJbpERBwsKhA6BVY9ryLYH3EvIiKOdUfExRNdlhDcItFVXS7fswugW7duxMWVn0fp9ddfp3//c/MATJkyha1bt/K///0Pb2/vWoulQavGdJp4E6UFRdz+w9yy8mM/7GX9PbNqbb8i4hruioAfjtsfr16Z7sEwtq1jY/JE/ZvDyDD4b7o9sXX+Z7YBCPODJzs5KTgRkTruxRh4YBPkFJe/MWHEvvxUF/v7tIiIOFb3xnBvW/tTzStrQzerD89e5aTgHMwtkl2/lZ+fT2JiIpMnTwZg9+7d/PWvf6V9+/b07t0bgIiICP773//W+L5zfznJuyG31/h25dLkJsVzcOEfwGbDv+tAWo6dWW598emj7JoUQZd5ydRr3IqCIykcfPNxrMWFNBv+BwJ71v5cbuLZzEb4Z094PxWWpcGJX59k0tgbbo+A+9pCPZNzY/QEBgNM6QodG8LSffbHJAP4me1JsPHt1KtLRMRZwv1hyfWwMBlW/wLFv2a8ujSC+9vB9c2dG5+ISF32ZCeIDIAlKZDy64xL9U0wojU80B6Caq9vkEtxy2TXzp07KS0tLZucvnPnzthsFxtYJJ7At00MHefYJ/RKmj6I0rwsTL4BZeuPf/FvGrTvWbZ85MMXiHj6Q8x+gY4OVTyYl9HemL83Eo7/On9XUx97IkxqjsFgHwZzWxgcL7B/mWrqo2SiiIgraNkA/hwNT3eB61fZy97u59SQREQEexv65lC4qRVcu9Jetu5G8KljbWi3THb17NlTya06ymD2AsBWWopXUAuM3r5l60pyMinJPol303AArMVFFJ08yP5X7wODgbDH5uMV2MwZYYuHMhkgxPfi9aR6DAZ7l2sREXE9vm75bUJExPOd/8DFupboAjeYoF7ktzK+Wcrux6MwNQjEYDrXwjqxai5Nbny4bLkk6yT56T8RPnkRzW6ZzNHlf3VGuCIiIiIiIiLiQEp2idsJ6n83nefupTjjMPn7dwFgLcyn4ODP+LaJKatnatCQ+mFXYfZrhF/nfhQcSnRWyCIiIiIiIiLiIOp4LG7FWlyI0csbg9GIqb4/hno+ABQe30/h0VSSZ9xIfvouSnJOEzl1BcZ6PliLCyk4uId6vw5vFBERERERERHPpWSXuJUz8Z9zfNVcsFrx63w91oJcTm1YQvCAe+n48vcA7P/X/bS4x/6UxmYjniJp+iAMRhPhv3/XiZGLiIiIiIiIiCMo2SVupVGf0TTqM7pcmW+b6HLL5ye1AqIHExA92AGRiYiIiIiIiIgr0JxdIiIiIiIiIiLiMZTsEhERERERERERj6Fkl4iIiIiIiIiIeAwlu0RERERERERExGNognpxOZEBsKCP8/YtIiKVi42NZfz48YSFhREWFsaiRYvKrV+yZAkhISEMHjyYfv36sX37dvbu3UurVq3K1Zs8eTIJCQkUFxeTm5tLQkICd911F0ePHiU7O5vQ0FA+++wzxo8fz9tvv43BYHDkYTrd5Zzn2bNns3r1aqKjo/n3v/9drt64ceNITU3F29ubxYsX07JlS4YOHUp+fj4BAQEsW7YMHx8fHn74YRYsWODIQ5Q6pDbfNwAKCgqIiIjg448/pm/fvjz22GP8/e9/p0GDBo46RBERcUHq2SUux88LooOd8+Pn5eyjFxFxbRMmTCA2NpaGDRsSFxdXbt3KlSsZNGgQAB9//DG33357pdt49dVXiY2N5c9//jM33XQTAB9++CGxsbFMmjSprKxnz55s2LChFo/GdV3KeT527Bjbtm3j22+/xWw2s2XLlnL1XnjhBTZt2sSUKVOYO3cuAPPnz+fbb7/ltttuY+nSpZjNZpo0aUJqaqrDjk3qntp63wB455136NSpU9nyLbfcwrJly2rhKERExJ0o2SUiIiKXrUuXLhw6dKhsOSMjAx8fn7JeWM2bN7/oNlasWMGIESPKla1cuZJbbrkFgAEDBrBq1aoajNr9VHWet2zZQv/+/QEYOHAg8fHx5V4bHh4OgMlkKvt3qazMYrGwevXqWj4SkZp/3ygtLWXTpk3069evbP3111/PmjVrajhyERFxN0p2iYiIyGWLi4sjMjKybDklJYWwsLDL2saWLVvo0aNH2XJOTg7Z2dmEhIQAEBYWRlJSUs0E7KaqOs+ZmZkEBNjH3/v7+5OZmVnh9VarldmzZzNhwoSysry8PN566y3uuOMOQOdZHKem3zc+/PDDCj3BfH19OX36dPWDFRERt6Zkl4iIiFyyhQsXYrFYaNmyJdHR0Ve8na1btxITE1NuPq41a9Zw44031kCU7u9SznNgYCBZWVkAZGdnExgYWKHO9OnTueuuu2jTpk1Z2cMPP8yMGTNo2LBhbYQuUkFtvG/YbDY+++yzCr1DRUREQMkuERERuQxn596ZOXNmufLIyEjS09MveTuVDWH8bVl6ejrt2rWrXsBu6lLO8zXXXMPGjRsB2LBhQ7lecgCffPIJGRkZPPDAA2Vl//znP+nUqRNDhgwpK6vL51kcozbeN3Jycti3bx833XQT7733Hk8//TQFBQXk5eVVmvgVEZG6RckuERERqbagoCDy8vLKlseOHcvatWsZM2YMX331FQBPPfVU2fqvv/66bFJqsM+9k5SUVG6i6Q0bNjBs2DAHRO8+zj/PzZs3p2vXrvTr14/CwkK6d+/O0aNHefnllwH44x//yI4dO7BYLMyaNYuioiL+9Kc/8eWXX2KxWFi4cCFgf1qezrM4Q3XeN/z9/dm6dStffvklY8eO5R//+Ac+Pj5s3LiRoUOHOvZARETE5RhsNpvN2UG4qmPxe1k9Ypqzw/AYw1bMpFmPjs4OQ0RELqD75/bfW4Zf2esXL15MixYtGDx4cI3EM378eBYuXIjR6Fn35lzpPJeUlPDQQw+VJb48TXXPtVya6pznmn7feOyxx5gzZw5+fn41sj1XouvZMXSeHUPn2THc/Txv3ryZvn37smnTJvr06XNZrzXXUkwiIiJSx9x33301ur133nmnRrfnKWryPJvNZo9NdIl7qOn3jblz59bo9kRExD151q1SERERERERERGp05TsEhERERERERERj6Fkl4iIiIiIiIiIeAwlu0RERERERERExGNognpxOTnFkJLlnH1HBoCfl3P2LSIiIiIiIiLVp2SXuJyULJi42Tn7XtAHooOds28RERERERERqT4NYxQREREREREREY+hZJeIiIiIiIiIiHgMJbtERERERERERMRjKNklIiIiIiIiIiIeQxPUi0dInGohd28cBrMXBpMZn5YdaTF2FgHdBjk7NBERERERERFxIPXsEo/RYuwsYpbl0HXRMRp06EXq7FspzT3j7LBERERERERExIGU7BKPY/TypvGQCVgLcig4kuLscERERERERETEgZTsEo9jLczn5LoFGH0D8GnRztnhiIiIiIiIiIgDuU2yq6ioiOnTpxMaGkr9+vUZOHAg8fHxGAwGVqxY4ezwxAUcXjqdhLsD2fVgOHlpO4ic9gUm3wBnhyUiIiIiIiIiDuQWE9TbbDZGjx5NfHw8M2bMIDIykqVLlzJy5EgAYmJiHBrPdX+dSOiQ7ngF+FKck0/6F3Fseek9rMUlDo1Dymtx90s0H/m0s8MQEXE7P52Gpannln+3DkaFw+hw8PNyVlSeZ382vL/v3PKQL2FEa7irDTT2cV5cIlciqwg+SoNP0s+VTd8G97SBjoFOC0tERARwk2TX/PnzWbVqFQkJCXTu3BkAi8VCREQEQUFBtG7d2qHx7HnnS7a8uISS/EK8g/yxzH+Krk/eRsIrHzk0DhERkepadRBmbAcM58qO5sPcPfZ1b/WBQG+nhecxtpyEJ7+HEuu5stNFsCgFVv56nlv7OS8+kctxsgAmboJf8sqXr/kF1h6C2dfAoBbOiU1ERATcZBjjnDlzGDt2bFmiC8BkMhEREVHWq+vWW2+lW7duxMTE0K9fPxISEmotnjNJv1CSX2hfMBiwWW34twmptf2JiIjUhl9yYUYCWAGrreL69ByYucPRUXme3BJ4Kt6e6LL+Zp0Ne9Lr2R/BVsm/gYgr+st2OJxXsfzse8nUrXA83+FhiYiIlHH5ZFdycjJpaWmMGjWqwrqDBw+WJbsWLVrEjh072L59O08//TQPPPBArcZ11eO3ck/KEsb89DZBncP4ef4Xtbo/ERGRmvbJ/qoTLFbgm6NwtJIvtXLpVh+0J7x+m+g6y2qDlGzYkeHQsESuyP4c+OHEha9nG1Bqg/+mX6CCiIiIA7j8MMZDhw4B0LRp03Lle/fuJT09vSzZ1bBhw7J1Z86cwWi89Dyev78/RUVFFcrbmhvxbEDfSl+z6/XP2PX6ZzRs15I2t/Uj//jpS95fXTVggIXUkoufJ98OvWg7M/aytt1h1uXVv5ABAyzkJcbVyLZERFxd5Ms/Uj+8a5V1bEC3EQ+QufF9xwTlgVo/9QEB1w7HYLpws8tmtXLL72dx/OOZDozM83X5qAAAb29NilZTgoY8SMtJr1dZx2qz8erK73mym8UxQdURup4dQ+fZMXSeHcPdz7PVeqFbKxfn8j27goODAUhJSSkrs9lsPPfcc1it1nKT00+cOJHWrVszdepUFi1a5JD4ziQfImN3Ov3+/aRD9iciIlJTqkq+lKtnNNVyJB7OaAaD4SKVbDrP4hYu5X3DYDDoehYREady+Z5dUVFRREZGMmXKFLy8vPDz82PevHls374dX19fOnToUFZ3wYIFACxZsoRnnnmGVatWXdI+srOzKy0/Fr+X1SOmXfT1Ri8TAZqz66I2bIilWY+OF62XcAombnZAQJXYsCGW6GDn7FtExNFe2A7/O3jh4UhnbfzoLdo3fMshMXmiBYnwn8Sq6xiMJhbNmcKAxVMcE1Qd0f1z++/CwkLnBuJBdmbAA5uqrmMExg3pwXNP6bzXJF3PjqHz7Bg6z47h7ud58+bN9O1b+Wi7i3H5nl1ms5nly5cTEhLCuHHjmDx5MsOGDaN///507dq10uGK9957Lxs2bODUqVM1Ho+Xvy+Rd1ioF+ALQKOoMLpNHsXhWM3gKyIi7mVUeNWJLiNwVSNo37CKSnJRt4aBsYqOXUYg2Bv6NXNYSCJX7KpG0Na/6i8RVuzvLyIiIs7i8j27ALp160ZcXPl5lF5//XX69+8PQE5ODqdPnyY0NBSAlStXEhQURFBQUM0HY7PRZtT1XDtjHMZ6ZgpOZpG+6gcSXl5W8/sSERGpRV0awbhIWJQCBuzzc51lBHzNMK2bk4LzII194Lmr4K877ef1/ASjEXsibObVYHb5W5Ai9hG5L1wND26CwtLy1/PZ95GHO0BkgJMCFBERwU2SXb+Vn59PYmIikydPBiA3N5fRo0eTm5uLyWQiKCiIlStXYrjo/BiXrzgnn7V3vljj2xUREXGGx6MgtIE94XUw115mNMDA5vBIFIT5OTc+TzEq3N57a0ES7D1zrrxHE3i4oz3xKOIuOjaEd/vBvL32J7aeTZSH+cH4dnBzqFPDExERcc9k186dOyktLS2bnL5Zs2Z8//33To5KHKEkO4Pkvwyh4FAiMctyyq07s+1Ljnz4Asb6/oQ/+S71glsAUHz6KLsmRdBlXjL1GrdyRtgiIi7LYLAPsxvRGvbnQH4phNSHRt7OjszzWELsP7/kwpkiaOIDTes7OyqRK9M2AP7RA04VwLECe0/QsAaX8CwGERERB3DLZFfPnj2x2WwXrygex1Tfn3YvrGPf3++osO7oJ3NoP3MDhUdSOPbpHEIf/BcAx7/4Nw3a93R0qCIibsVggAh/Z0dRN7RqYP8R8QTBPvYfERERV6LZIcStGMxemP0rn4vNYDJjrOeDT+vO5CbHA1CSk0lJ9km8m4Y7MEoRERERERERcRYlu8Rj2EqLKck6Rc7PmyjJzgDgxKq5NLnxYSdHJiIiIiIiIiKOomSXeIyW98wkdc7tnP72A+qHdsJamE/BwZ/xbRPj7NBERERERERExEHccs4ukcr4depLh1kbyE2KJ3vn1xQe30/h0VSSZ9xIfvouSnJOEzl1hbPDFBEREREREZFapGSXuJ2k6YPJS9tO0vTBtBgzg5y9cTS/7RkOf/giOT/F4hXckrBH38To7UvHl+1P6dz/r/tpcc9MJ0cuIiIiIiIiIrVNyS5xO+1fWl9u2a9TXwBa3PVn4M+Vvib89+/WclQiIiIiIiIi4go0Z5eIiIiIiIiIiHgMJbtERERERERERMRjKNklIiIiIiIiIiIeQ3N2uZi+rz5GcW4BP0xd6OxQKrgnZQmrb/0zGT+l1ep+IgNgQZ9a3UWV+xYRERHPFxsby/jx4wkLCyMsLIxFixaVW79kyRJCQkIIDQ3lgQcewGg00r17d/7v//6vXL0dO3bw6KOPUq9ePRYvXkxoaCi33HILWVlZmM1mli1bRmBgIA8//DALFixw5CGKiIjUWUp21aLmvTsT/dQdBHdtA8CpnftI+MdHHI3b7eTI7KKfuoPgbm356r6/XlL99yPvreWI7Py8IDrYIbsSERGROmzChAlMmzaNJ598kri4OHr16lW2buXKlSxbtoyMjAzWrFmDn58f9957L0lJSbRv376s3osvvsh///tfjhw5wpw5c3j99df59NNP8fLyYsmSJSxdupQnn3ySJk2akJqaStu2bZ1xqCIiInWKhjHWklaDr2HI0mkcXL+Vj65+iI+ufoiD67cy5INptBp0daWvMZhq7p/DYDbV2LZEREREPFmXLl04dOhQ2XJGRgY+Pj4YDAaCg4Px8/MDwGQyYTAYyr02NzeXpk2b0q1bN1JSUgDw8vICoKCggKioKAAsFgurV692xOGIiLi92NhYIiIisFgsjBs3rsL6WbNmkZycXOU2nnjiCfr168fs2bMvWOfVV19l8ODBZGRkcM0115S935/15ptvMmjQICwWC8XFxezZs4c5c+Zc2UGJQynZVUt6znyAlI9j2T3vc4qz8yjOzmP3vM9JXf4NPWY+AMDt8W/QdfIobvp8FmP3vU/jbvY7fV4NfLDMf4p7kpdw6zf/RwtLt4vur3mvztyTsoT2Ywdz+4/zGL7+HwCEXN+V362Zw92Jixj+1Su0vvFaAFrfeC1XPTmSlgOiuSdlCfekLMFYr+qOfvcfWU5wt7YYTEbuTlpMYIdQ+757d+b+I8sJ+539bmj9poHcd+BDvPzqX9nJExEREXGguLg4IiMjy5ZTUlIICwsrV2f37t2cPHmSdu3alSu3Wq0V/s7IyKB379689tprZcmusLAwkpKSausQREQ8zoQJE4iNjaVhw4bExcWVlVutVnbu3Fnh/fh8W7ZswWw28+2337Jt2zaOHTtWoU5xcTEJCQkA+Pv7s27dOq677rqy9QcOHGDXrl189dVXxMbG4uXlRVRUFNu2bcNms9XcgUqtULKrFgS0bYF/WDPSPttcYd2+/24iILw5AW1CAIgcbWHzU2/wfuS9nPp1LqyIW/uy77/fsrTjOHb++78MfPtZfJsHXXS/5vreNI6O5LPrJ/PFsOfwD2/O4EXP89Prn/FBp/FsnfUe/ef9gaDO4Rz48kd2vfZfDm1I4P3Ie3k/8l6sRSWXdHy2UivHfthDSN+rAAjpdxVZaUcI6dvFvtz3Kk7t3EdxTv4lbU9ERETEGRYuXIjFYqFly5ZER0dfsF5WVhaPP/448+fPr7DOaDRW+DsoKIjvvvuOmTNnVpjjS0RELs9ve9/u2LGj3HDyyvzwww8MHDgQgP79+7N169YKdZYsWcKYMWMAe4/coKDy37nXrl1Lbm4uAwcOZMaMGWXlbdq0Yfdu15iaSC5Mya5a4BPkD0D+sYwK6/KPn7bXCbbPhJ64ZB1nkg9hs1rLkk1Hv/+ZA6vjsZVa2bd8I6d/Tif8ll4VtvVbBqORrbPfpyS/kNL8IiJG9ObYD3vYvzIOW6mVQ19v58DaLbQd3b/ax3h000/lklsJ//ioLPnVvE8Xjmz+qdr7EBEREalNZ3sNzJw5s1x5ZGQk6enpANhsNh544AFefPFFWrRoUWEbvr6+nDhxgp07dxIZGYnVaqWkxN6ma9iwIT4+PgCkp6dX2QtBREQq99vet8nJyYSHh1f5mszMTAIC7N+5/f39yczMLLfearWyZs0abrjhhgtu4/jx4xgMBr7++mvS0tLKeoGpp6570AT1taAgIxuA+s2COJNyuNy6+k0b2eucygIg99CJCq/P/aV8Wc7BE5fUs6skv5DCX/cN4BsSTM5vt5V+DL+wZpdwFFU7smkXXSePol6ALwFtWpD22Waufn4MviFBhPTtwndP/afa+xARERFxhqCgIPLy8gD7vDFff/01J0+eBOCVV16hZcuWLFmyhGeeeYbp06dz66234uXlxeLFi8nNzeV3v/sdRqMRb29v3n333bLtTJgwwVmHJCLidhYuXMj69evp27fvBXvf/vzzzzz66KPlym688UYCAwPJyrJ/587Ozq7wcJBPP/2U4cOHV7n/hg0b0r+/vaPI9ddfT2JiYpW9gMW1KNlVC7JSD5N94BgRw3tz9Dc9nCJG9Cb7wDGy9h0BwGatONa3Qasm5Zb9QptwYtvFM8e28+aMAMg7cormvTr/ZltNyTtyqtL6lyNj935sViudHvwdx3/ci81q5cimn2h/92B8mzbi+I97r3jbIiIiIrXNYrFgsVguuH748OGsX7++bOLi33rmmWcAiImJYfPm8lNXfPPNN+WWS0pKOH78eLmeCSIiUrWzT8z9rXbt2vHpp58C0KlTJ2JjYyvU+fHHH/nggw+45ZZb2LhxI3fccUe59YmJicTGxrJkyRISEhJYsGABEydOLFenV69eLF26FIBdu3aVDXlMT0+nT58+NXGIUos0jLGWxP/5XSLvHEDnh27By68+Xv6+dHrod0TeMYD46e9U+drm13UidGh3DCYjbW7rR6NOYez/3/eXHUPaiu9o1jOKsJt7YjAaaTkgmtAbupO6fCMA+SfP0KBl4yt+CuTR73bTadLNHNm0C4Ajm3fRadLNHN+aRGlh8RVtU0RERMQV3HfffQwePLhGtmU2m1m4cGGNbEtEpK7r1q0biYmJVda59tprKSwspF+/fnTr1o1mzZpx9OhRXn75ZQCmTp3KunXr+PLLL4mOjmbixIkMHjyY7du3M3jwYH766SeuvvpqSkpKsFgs5OXl0bNnT8D+EJMuXbrU+nFK9ahnVy05uOZH1o+dTbc/3k70M/Ys8qmd+1g/dnZZcuhC0j7bRNvR/bl+7u/JPXKKDRP/Qd7hU5cdQ/b+o3z9wN+5+k/30Of/HiP3lxNsfPw1Mn6dCH//yjja3NqXu356G4PBwIddJ1zyJPUAR77dRfjvenFkk7332pFNP1EvoIHm6xIREREREZErVlXvW6PRSNeuXUlOTq5yLsS5c+eWW27evHlZr9zzrV+/vtzv87366qvllvfs2cM111yDwWC4yBGIsynZVYuObNpVZWJreY9HK5Rtmjy3kpoXdzRuN+9H3luh/NCGBA5tSKj0NUWZOXw56i+XtH3Dr08XOj8Zlrh4LYmL15Yt5x87zbsht19G1CIiIiIiIiKXp7LhjY4QFRVFVFSUU/Ytl0fDGOWSNOochrW4pNIJ9UVEREREREREXIV6drmREbH/h1+rxhXKD67dysZHX6329ge/P5VmPTtWKC/OKcDoZWLr7KUUZeVVez8iIiIiIiIiIrVFyS43ssLyh1rd/vp7ZtXq9kVEREREREREapuSXeJycoohJcs5+44MAD8v5+xbRERERERERKpPyS5xOSlZMHGzc/a9oA9EBztn3yIiIiIiIiJSfZqgXkREREREREREPIaSXSIiIiIiIiIi4jGU7BIREREREREREY+hObvEIyROtZC7Nw6D2QuDyYxPy460GDuLgG6DnB2aiIiIiIiIiDiQenaJx2gxdhYxy3LouugYDTr0InX2rZTmnnF2WCIiIiIiIiLiQEp2iccxennTeMgErAU5FBxJcXY4IiIiIiIiIuJASnaJx7EW5nNy3QKMvgH4tGjn7HBERERERERExIHcItlVVFTE9OnTCQ0NpX79+gwcOJD4+HgMBgMrVqxwdnjiIg4vnU7C3YHsejCcvLQdRE77ApNvgLPDEhEREREREREHcvkJ6m02G6NHjyY+Pp4ZM2YQGRnJ0qVLGTlyJAAxMTFOicvkU48RX79C/aaBvB95r1NikPJa3P0SzUc+7ewwnMpqg68Ow0dpkHgGTAbo1RTubAPdgpwdnWeJO24/z9tO2Ze7BcEdEdCnKRgMzo1N5HKlZcOyNFh/GApKoXUDuC0cfhcKPiZnRydyeTKL4NP9sOLAubI39sDoCGji47SwREQE2HrS3uY465HvYHQ4DAhRG1pqlssnu+bPn8+qVatISEigc+fOAFgsFiIiIggKCqJ169ZOiSvmmTvJ+eUk9ZsGOmX/Ir9VYoVp2+xfVo2A9dfy9Udg7WH4Q2e4p60zI/QMNhu89jMsSS1/nr8/Ad8dh7si4Kku+rAW9/HNUXjuR3uy/Oz1nJwFf9sJnx+AN3qBn5dTQxS5ZL/kwqTNcLygfPk7ybB8P8zrDR0aOiU0EZE6b2ESzNsLxvPayVtPwo8n4eZW8JeY8utEqsPlhzHOmTOHsWPHliW6AEwmExERERV6db3wwgsYDAZ++umnWo0puGsbWg6I5qe5n9XqfkQux6IUe6ILzn1hBfsXWID/223/MJHqWXPInuiCys/zh2nwxUGHhyVyRY7mwfNboNRW/nr+9XJmT6Y96SXiDmw2eCoeThZUsg7IKYbffw/F1orrRUSkdm0+Zk90wbl2M5xrf/zvF/hwn8PDEg/m0smu5ORk0tLSGDVqVIV1Bw8eLJfs2rZtG99//z1hYWG1GpPBZKT3Px7m+ykLsBaV1Oq+RC5VifXiHw5G9AFSE5buq/qN0wC8n2r/0iXi6j5Nt79/XOhytWHvGVpZ8kDE1Ww9BanZ5RO357MCJwthwxFHRiUiIgAf7Lt4r62l+8onwkSqw6WHMR46dAiApk2blivfu3cv6enpZcmuwsJCHnvsMT744AMsFstl78ff35+ioqIK5W3NjXg2oG+5si6PjuDUrjSOfb+H5r06V3iNXNiAARZSS05ftJ5vh160nRl7WdvuMOvy6l/IgAEW8hLjamRbjuTTJoZ2c76vso4V+OpAAd79NH7jShkbBNL53WNV1rEBKdng17w1JZlV1xVxtsiX4/EJ64qhinG3Vht0/t39ZH77gQMjE7l8ze+ZRePhf8BgvPBEc7bSEh55ZSm/zH3QgZGJ1IwuH9nvPHh7a/K52qTzXAuMRrp8mIvBUHVfm6P5ENimK4WHEx0UmOdz9+vZar3y7tgu3bMrODgYgJSUlLIym83Gc889h9VqLUt2/fnPf2bs2LGEh4fXajz+4c3pcN9Qtry0pFb3I3K5DKZLm1DnUutJ5S7n/BnM9WoxEpGaYTDVqzLRVVZP17O4AYPZC2wXbxTrehYRcSyD0XzRRFdZXbO+r0jNcOmeXVFRUURGRjJlyhS8vLzw8/Nj3rx5bN++HV9fXzp06EBcXBxbtmzhb3/72xXvJzs7u9LyY/F7WT1iWtlysx4dqd+4Ibdtfg0Ao9mEV4P63LX7bTZMeJlj3++54hjqgg0bYmnWo+NF6yWcgombHRBQJTZsiCU62Dn7ro7MIrhxDZRU0e3XAEQGmthaWOiwuDxNqc1+nk9X7AhaToAXnD6YgpdL304QgalbYd3hiw8Z+OqD+XRuNN8xQYlcoc8PwIsJVdcxmMxMffAuxv/9LofEJFKTun9u/12otlyt0nmuHbeuh0N5F546AcDbCIf3bKWBS2cp3Iu7X8+bN2+mb9++F69YCZf+KmY2m1m+fDkhISGMGzeOyZMnM2zYMPr370/Xrl0xGo1888037Nmzh4iICMLDw/nll1+44YYbWLt2bY3Hk7byOz7p/TifD36azwc/zean/kNxXgGfD36aE9uSa3x/IpcqsB4MaVn1f2gbcEeEoyLyTCYD3B5uTxxeiAEYGYYSXeIWbg+vOtFlBNoHQKdABwUkUg1DW4Cvqer3aJMBhoc6LCQREfnV6IiqE11G4OZQlOiSGuPyX8e6detGXFwceXl5JCYm8sgjj7B9+/ayIYzPP/88hw8fZv/+/ezfv59WrVqxZs0ahg4dWuOxlOYXkXcko+yn8FQW2GzkHcnQZPXidI9HQbDPhf9Tdw+GW1o7NCSPNDYS2vpX/mXKCIT5wf3tHB2VyJWJDoJRF3iuixF70nZaNFzCSEcRp/Mxw/Ro+9+/vWTPLv+xi/2zUkREHOv2cOja6MJt6Gb14aEODg5KPJrLJ7t+Kz8/n8TExHJPYnSWo3G7eT/yXmeHIQLYPyDe6QcDQ8o/6cTXBPe0gX9dp95GNaGBGd7qC7eGQb3zzqeX0X43amFf8NdUA+ImDAZ4ris82QmCvMuvu6YxLOynXl3iXoa0hP/rCZEB5ctDG8Csa+BO9XAWEXEKbxPM7WUfaeJz3nNETAb7e/c7/XQzQmqW23US3LlzJ6WlpRdMdu3fv9+xAYlD5fy8iV/eeRoMRhr1HkWzW58qW5f0l6HYivIx1g+gzTPLMNX3Y/djUZgDmwHQ9vlPMfsHOSt0h2heH/52LZwsgBt/Hcm75gao73b/012bvxdM7WZPEAxYbS/7cig01JzH4oaMBrgvEu5uA9d9YS/7bBC0auDcuESuVN9m0KcppGbbPw8D60GHhuqhKCLibPXN8MxV8GgU7Mm0T6UQGVDxhptITXC7r8A9e/bEZrvITLriseo1a0P72RsxetUjceoAmgx7BKO3LwBhj87Hu1k4J9ctJGPjUprcMAmvoJa0f2m9k6N2vMbn3RVRoqv2nN+DS4kucXfm83oqKtEl7s5gsH+B+m0PLxERcb4GZuje2NlRiKfToCZxK/WCW2D0smcVDEYTnPcIW+9m4fY/jKay27fFZ46R+Kfr+eXdZ5UkFREREREREakDlOwSt5SVsA7vkLYY65Uf2G0tzOPk2rcI6nMHAB1mb6TDXzdiLSoga+sqZ4QqIiIiIiIiIg6kZJe4naKTv3B0+V9pNf6VCuvS33iYFmNmYGrQEACzXyMAAnvcQv6B3Q6NU0REREREREQcT8kucSvW4kL2/+t+Wj88D1N9v3Lrjq34J/VDOxEQPQQAW0kx1uJCAHL2xuHdTI9gEhEREREREfF0mrpa3ErGN0spOPgz6fMeAqDl2FkUHt1Ho753cmjxn2jQsRdntn9JsOVeAnuOIHnGDRi9G1CvcSgho6c4OXoRERERERERqW1KdolbaTx4PI0Hjy9X5hfVB4CrPymsUD/qn1sdEpeIiIiIiIiIuAYNYxQREREREREREY+hZJeIiIiIiIiIiHgMJbtERERERERERMRjaM4ucTmRAbCgj/P2LSIiIiLiTmJjYxk/fjxhYWGEhYWxaNGicuuXLFlCSEgIHTp04M4778RsNtO2bVvefvttDAZDWb0TJ04wadIkMjMzGTNmDJMmTcJisZStGzJkCP/4xz94+OGHWbBggSMPUUTksijZJS7Hzwuig50dhYiIiIiI+5gwYQLTpk3jySefJC4ujl69epWtW7lyJcuWLcNqtbJ582YMBgMTJkxg165ddO3atazeiy++yKuvvkpYWFhZWWxsLAB/+tOfGDBgAGazmSZNmpCamkrbtm0ddnwiIpdDwxhF6qjY2FgiIiKwWCyMGzeuwvpZs2aRnJxc5TaeeOIJ+vXrx+zZsyus279/PyEhIVgsFu677z7i4+Pp06cPvXv3Ztq0aQAUFxdzxx13YLFYePnllwHYs2cPc+bMqYEjFBEREal7unTpwqFDh8qWMzIy8PHxwWAwYDKZynpyeXt706JFi3KvTUpK4vnnn2fo0KEkJiaWWxcbG1vWy8tisbB69eraPRAXVdtt6LNeffVVBg8eTEZGBtdccw1+fn7l1r/55psMGjQIi8VCcXGx2tAiv6Fkl0gdNmHCBGJjY2nYsCFxcXFl5VarlZ07d9KuXbsLvnbLli2YzWa+/fZbtm3bxrFjxyrUufnmm4mNjWXx4sXExMSwefNmvvvuO+Li4sjKyuLTTz+ld+/exMbGsnXrVk6cOEFUVBTbtm3DZrPVyjGLiIiIeLK4uDgiIyPLllNSUsr11Pr666/p2rUrR48epWHDhuVeu2nTJmbOnMncuXOZOnVqhW3Uq1cPgLCwMJKSkmr5SFxXbbehi4uLSUhIAMDf359169Zx3XXXla0/cOAAu3bt4quvviI2NhYvLy+1oUV+Q8kuEalwB3DHjh20b9++ytf88MMPDBw4EID+/fuzdevWCnXWrFlDv379eP/99/Hy8gKgtLSUFi1a4OvrS1paGldddRUAUVFR/PjjjwC0adOG3bt318ixiYiIiNQFCxcuxGKx0LJlS6Kjoy9Yb+DAgezcuZOwsDC+/PLLcus6d+5M27ZtadeuHWfOnCkrX7FiBSNGjKit0N1WbbWhlyxZwpgxYwDw8vIiKCio3Pq1a9eSm5vLwIEDmTFjRlm52tAi5yjZJSIV7gAmJycTHh5e5WsyMzMJCLDP6O/v709mZma59SEhISQmJrJ27VrefPNNTp06xdKlS4mKiiIwMBCz2UyHDh345ptvsNlsfPvtt2WNqrp+t1BERETkcp3tbTRz5sxy5ZGRkaSnpwNQVFRUVt6wYUN8fHzK1Q0PD+fkyZMcO3YMX1/fsvJVq1Zx0003lS2np6dX2XuprqiNNrTVamXNmjXccMMNF9zG8ePHMRgMfP3116SlpZX1AlMbWuQcTVAvUoctXLiQ9evX07dv3wveAfz555959NFHy5XdeOONBAYGkpWVBUB2dnaFCUq9vb3L/u7Xrx+pqancfffd3HXXXYwePZpdu3YxfPhw1q5dy5AhQ2jSpAlNmzat2QMUERERqeOCgoLIy8sDID4+nilTpmA0GomMjGTQoEEcPXqUJUuW8MwzzzBt2jRuu+02SkpKeO211wA4efIkXl5e5YY8xsbGMmHCBKccjyuozTb0p59+yvDhw6vcf8OGDenfvz8A119/PYmJiVX25hOpi5TsEqnDzj6157fatWvHp59+CkCnTp3KnsJzvh9//JEPPviAW265hY0bN3LHHXeUW5+Tk4Ofnx82m40tW7YwadIkAIxGI/7+/vj4+GAymZg3bx42m41x48aVPTUoPT2dPn361PDRioiIiHgmi8VSNnl8ZYYPH8769esZPHgwGzduLLeuefPmPPPMMwB07dq1wvrGjRuXG+5YUlLC8ePHy/Voqmtqsw2dmJhIbGwsS5YsISEhgQULFjBx4sRydXr16sXSpUsB2LVrV9mQR7WhRc7RMEYRqaBbt24VnsDzW9deey2FhYX069ePbt260axZM44ePVr2VMXvvvuO7t2707t3b4YOHUp8fDwWi4Xrr7+eVq1a0a5dOw4cOIDFYmHQoEHcfvvtZd3lU1JS6NKlS60fp4iIiEhdcN999zF48OAa2ZbZbGbhwoU1si1PUxNt6KlTp7Ju3Tq+/PJLoqOjmThxIoMHD2b79u0MHjyYn376iauvvpqSkhIsFgt5eXn07NkTUBta5Hzq2SVSR1V1B9BoNNK1a1eSk5OrnI9h7ty55ZbPvzM4dOhQhg4dWm796NGjyy23bt26wh2vPXv2cM0115Q9FltERERExFXUdhv6fOvXry/3+3yvvvpquWW1oUXKU7JLRCpVWddsR4iKiiIqKsop+xYRERERqQ61oUVcg4YxioiIiIiIiIiIx1CyS0REREREREREPIaSXSIiIiIiIiIi4jE0Z5e4nJxiSMlyzr4jA8DPyzn7FhEREREREZHqU7JLXE5KFkzc7Jx9L+gD0cHO2beIiIiIiIiIVJ+GMYqIiIiIiIiIiMdQsktERERERERERDyGkl0iIiIiIiIiIuIxNGeXeITEqRZy98ZhMHthMJnxadmRFmNnEdBtkLNDExEREREREREHUs8u8Rgtxs4iZlkOXRcdo0GHXqTOvpXS3DPODktEREREREREHEjJLvE4Ri9vGg+ZgLUgh4IjKc4OR0REREREREQcSMku8TjWwnxOrluA0TcAnxbtnB2OiIiIiIiIiDiQ2yS7ioqKmD59OqGhodSvX5+BAwcSHx+PwWBgxYoVzg5PXMDhpdNJuDuQXQ+Gk5e2g8hpX2DyDXB2WCIiIiIiIiLiQG4xQb3NZmP06NHEx8czY8YMIiMjWbp0KSNHjgQgJibGYbH0ffUxIkb2xVpcUlYW++ArHNqQ4LAYpHIt7n6J5iOfdnYYTmWzwQ8nYPn+c2V/2wm3h0Ok8n7ihtKy4eP9sOkYlFihQ0P79dy7KRgMzo5O5PJkFsJnB+DLXyCzCJr7wojWMKwl+LhFi0xEHM1qs38GfrL/XNkrP8HocGjt56yoPNO2U/Bx2rnlFxPgjnDoGOikgESkWtyiaTV//nxWrVpFQkICnTt3BsBisRAREUFQUBCtW7d2aDxJ763nh6kLHbpPkYux2uCvO+G/6WA8Lwnw6X57A2latP1LlYi7WHUQZiQANrD+WnayAL49Bje3gj/HgEkJL3ETKVnwyHdwuuhc2alC+Ok0fLAP/tMbgrydF5+IuJ4SK0zbBusPlx+O8+E+e1Jm1jUwqIXTwvMYNhu89jMsSS1/nr84AJ8fgD92hrvbOi08EblCbjGMcc6cOYwdO7Ys0QVgMpmIiIgo69UVHh5Ox44diY6OJjo6mjVr1jgrXBGn+GCfPdEF9sTXWVbABsxMgF0ZTghM5ArsPQMzttuvZet55Wf//t8vsETPnxA3UVQKT3wPZ4rKl599q96fDX/a4vCwRMTFvZVkT3RB+c9CG1Bqgylb7T2gpXpWHrQnuqDyNsc/d8P3xx0dlYhUl8snu5KTk0lLS2PUqFEV1h08eLDcEMbly5eTkJBAQkICN9xwQ63F1Oa2foz5+R1u3fgqXX9/GwaTy59G8XClNngvteo6BuDDtKrriLiKZfsuXuf9VPtdbxFX99UROFFQ/kvU+azA1lOQdMaRUYmIKysorfqz0Ia9R9JHattVi81mv3lWVUdxI/Y2h4i4F5cfxnjo0CEAmjZtWq587969pKen18h8Xf7+/hQVFVUob2tuxLMBfcuV/bxwFVteWkJBRjbBXdvQ/43JmLzrsf3vH1Y7Dk83YICF1JLTF63n26EXbWfGXta2O8y6vPoXMmCAhbzEuBrZliP5hHel3cs/VlnHCqzel8c/ejdyTFB1RJePCgDw9vZxciSepdM7RzH5VX2tni6Cxl37kZ8c76CoPJ+u59oROnkJDa+7DYPpws0um83KwIdf4MSnf3NgZCLiqhp0sdDmL1WPVLECS7cd5s/XRjgmKA/k1SSMjm8kVVnHCnx33IpPgwBsJcWOCUykhrh7285qvfI72y7fJSk4OBiAlJRz41VsNhvPPfccVqu1XLLrnnvuoWvXrjz66KNkZmbWSjwZu9IoOJUFNhundqSy/R/LiBjRp1b2JXKpjPXqX1I9g5cmhBH3YKh3aR/Il3rtiziTsV79iz9RwWrD6K3rWUTsLrltp8/Barnk82wwYjCrHS3iTly+Z1dUVBSRkZFMmTIFLy8v/Pz8mDdvHtu3b8fX15cOHToA8O233xIaGkphYSGTJ0/m8ccf57333rukfWRnVz7Y/Vj8XlaPmFb1i622qvu9SpkNG2Jp1qPjReslnIKJmx0QUCU2bIglOtg5+66OjEK4cW35ubp+ywCEB5jYWljosLjqgu6f238X6rzWqLtjITnr3JxGlTEAezavpZna+TVG13PteO1n+zCZKq9nk4n/vPQ8N737vMPiEhHXdTAHRn5ddR0jcE1oIxL0nn3F8kpg8JdQdJHOI8HekJd5Sk+CFrfj7m27zZs307dv34tXrITL9+wym80sX76ckJAQxo0bx+TJkxk2bBj9+/ena9euGI32QwgNDQXA29ubRx99lM2baydbEj68N17+vgA0igqj21Oj2f+F+w17E88S5A0Dmlf9H9oG3B7uoIBEqun28KoTA0agbzOU6BK3MLL1xRO3fmYYqKeqicivQv2ge3DVbTsrcLtGMFaLrxluDq36PBuA0eEX76ArIq7F5Xt2AXTr1o24uPIJpddff53+/fsDkJubS0lJCQ0bNsRms/Hhhx8SHR1dK7F0vP8Ges2ZhNHLRP6xTFKXf8POf/+3VvYlcjme6AQ/noSckoo9vAxAp0C4tbUzIhO5fL8LtT9xcWdGxSSB0QC+Jvh9J6eEJnLZQv1gfDt4J7niOgP2a/z5ruBjcnRkIuLKnuoCD2yCwtLKH3DRozEMUZK82h5sD5uOwalKHiRiBML94K42zohMRKrDLZJdv5Wfn09iYiKTJ08G4NixY4waNYrS0lJKS0vp1KkTb7zxRq3s+8vb/lIr2xWprlYN4J1+8Led9qTXWWYDDGsFT18FPm75P17qonomeP06++O+vzgIxee1PqOD7ImBcH/nxSdyuR7tCI3q2RNep897Jk5LX3iyk3p1iUhF7RrCwr4wZxfsyDhXXs8II1rD7zuD2eXH6bi+pvXhnb7w153w3fFzN9mMBnsy8dmrwM/LqSGKyBVwy6++O3fupLS0tGxy+jZt2rB9+3YnRyW17cD8J8hPSyDg6mGEjJ5SVn5y/ducXLsAc2BTIiYvweRr/wacty+BPU9fyzWf2p+akpv4A4fem4LNaqXVuDk0aN/DKcdRm8L8YF5vOJADSVn2RFd0EARqPk1xQ/XNMLUbPB4Fg760ly0faL/DKuJuDAa4uy3cEQHXfWEvW9AHugVpaIyIXFj7XxNe+7LtP15GuCZYyZea1twX/nUdHM6DPZn2Xrddg6Cxez7ATkRw02RXz549sdmqmv1CPE1u8hYMRjMd/votqX+7neLMY3gFNsNWWsKprxfR4W+bOLN1FSfXLaDZiD8AcOLLefi2uRqwP8Hz2IpXiPzzKox14ImErf3sPyKeoGG9c38r0SXu7vxeGO74QBQRcY42/vYfqV0tfO0/IuL+1PFV3EJu0g/4dx0IgH+X/uSlbAWgJOsk9Zq0xmA0Uj/sKnIT7XO7FRxOxhzQBFN9e6ug8Ggq1qJ8UmcOJ+2VeygtyHXOgYiIiIiIiIhIrVKyS9xCaW4mJt8AAIz1/SnNzQTAHNCEwiMpWIsKyNm9kdLcMwAcX/kvmt78eNnrSzKPU3gklbbTPse/22BOrn3L4ccgIiIiIiIiIrVPyS5xC6YGgZTmZQFgzc/G1CAQAIPJRLORz5D8wo3k79+BObAZRacOA+DVqPl5r29Ig/Y9MXp549/5egoOJTr8GERERERERESk9inZJW6hQfseZO/aAED27o34Rl5Ttq5Rr9voMCuWBu170vCaYRQc/Jm8tASSZ9xIXloCB+Y/iU+L9hSfPoLNZiM/fRfeTcOddCQiIiIiIiIiUpvccoJ6qXsatLuWU1+9S+Kf+hEQcwPFGUfI2r6W4AH3cuA/j1JwKBGf0E6ETngVg8lEQPRgAJKmD6b1pNcACOp/D0lTrsdYz5eIpz9w5uGIiIiIiIiISC1RskvcRuuH55Zb9m0T/Wv5Gxd8TfuX1pf9HTzgXoIH3FsrsYmIiIiIiIiIa9AwRhERERERERER8RhKdomIiIiIiIiIiMdQsktERERERERERDyG5uwSlxMZAAv6OG/fIjUlNjaW8ePHExYWRlhYGIsWLSq3fsmSJYSEhDB48GD69evH9u3b2bt3L61atSpXb8aMGaxYsYKGDRsyadIk7r77bk6cOMGkSZPIzMxkzJgxTJo0ifHjx/P2229jMBgceZhOV1Pn+ayYmBieeuopxo4dy/Tp05k0aRKhoaGOOBQREREREYe4nDb0559/zquvvorVamXx4sW0bt26rN64ceNITU3F29ubxYsX07JlSwYMGIDNZgPgjTfeoH379jz88MMsWLDAYcenZJe4HD8viA52dhQiNWPChAlMmzaNJ598kri4OHr16lW2buXKlSxbtgyAjz/+mOeff/6C2/n3v/9N3759y5ZffPFFXn31VcLCwsrKevbsyYYNGxg4cGAtHIlrq6nzvHr1aoKCgsqW77nnHt566y1efPHF2gteRERERMQJLqUNnZuby4cffshXX31V6U31F154gfDwcL766ivmzp3L7NmzMZlMrF+/vly9Jk2akJqaStu2bWv9uEDDGEVEHKJLly4cOnSobDkjIwMfH5+yD4zmzZtX+frJkydz8803s3//fgCSkpJ4/vnnGTp0KImJiQAMGDCAVatW1c4BuInqnuf333+fMWPGlC137NiRbdu21U6wIiIiIiIuoKo2dFxcHFarlSFDhvDYY49htVrLvTY8PBwAk8lU1ua22WxYLBYmTpxIQUEBABaLhdWrVzvmgFCyS0TEIeLi4oiMjCxbTklJKdcrqyq///3v2bJlCzNmzOC5554DYNOmTcycOZO5c+cydepUAMLCwkhKSqr54N1Idc7z5s2b6d69O2Zz+U7PRUVFNRqjiIiIiIgrqaoNffz4cU6ePMnatWtp3Lgxn3/+eYXXW61WZs+ezYQJEwBYvnw5sbGxREVFsXDhQsDx31WU7BIRqUULFy7EYrHQsmVLoqOjr2gbjRo1AuDaa6/l1KlTAHTu3Jm2bdvSrl07zpw5U1Phuq2aOM9vvvkmEydOrNnARERERERc1KW0oRs2bEi/fv0wGo1cf/31ZaNKzjd9+nTuuusu2rRpA5z7/jJ8+HB2795da/FXRXN2iYjUorPj4H8rMjKS9PT0S9pGdnY2/v7+7Nu3jwYNGgD27sInT56ktLQUX19fANLT02nXrl3NBe9GauI879u3j9tvv51Dhw5hNBrp168fYWFh1KtXr6bDFRERERFxuktpQ1977bUsXrwYgF27dpUNWzzrk08+ISMjg1mzZpWVnf3+EhcXR0REBOD47ypKdomIOEFQUBB5eXlly2PHjuXrr78mNTWVGTNmMGjQIJ566ileeeUVnnvuOXbs2IHRaGTu3LkATJs2jdtuu42SkhJee+01ADZs2MCwYcOccjyu6nLO86ZNmwB49913MZvNhIWFsXfv3ivuKSYiIiIi4o7Ob0M3bdqUq6++mv79+9OkSROWLl3K0aNHWbJkCc888wx//OMfadmyJRaLhSFDhjB16lQGDx5MvXr1CAwM5L333gPsT388O8zREQy2s8+DlAqOxe9l9YiKWU65MsNWzKRZj47ODkOkRnX/dcj6luGX/9rFixfTokULBg8eXCOxjB8/noULF2I0et4IdWed5+nTp/Pggw+We7yyJ6vOeZZLp/MsIiIijuAq31VKSkp46KGHyubvulSbN2+mb9++bNq0iT59+lzWa9WzS0TESe67774a3d4777xTo9vzFNU5zy+99FINRiIiIiIi4h5q8ruK2Wy+7ERXdXne7X8REREREREREamzlOwSERERERERERGPoWSXiIiIiIiIiIh4DCW7RERERERERETEYyjZJSIiIiIiIiIiHkNPYxSXk1MMKVnO2XdkAPh5OWffIiIiIiIiIlJ9SnaJy0nJgombnbPvBX0gOtg5+xYRERERERGR6tMwRhERERERERER8RhKdomIiIiIiIiIiMdQsktERERERERERDyGkl0iIiIiIiIiIuIxNEG9eITEqRZy98ZhMHthMJnxadmRFmNnEdBtkLNDExEREREREREHUs8u8Rgtxs4iZlkOXRcdo0GHXqTOvpXS3DPODktEREREREREHEjJLvE4Ri9vGg+ZgLUgh4IjKc4OR0REREREREQcyG2SXUVFRUyfPp3Q0FDq16/PwIEDiY+Px2AwsGLFCmeHJy7EWpjPyXULMPoG4NOinbPDEREREREREREHcos5u2w2G6NHjyY+Pp4ZM2YQGRnJ0qVLGTlyJAAxMTEOj6nVoKuJee4uAtq2oCQnn5/+s5Ld8z53eBxyzuGl0zn68UwMZm98QjsROe0LTL4Bzg5LPNT+HPhk/7nlv++C28Ohjb+zIvJMx/Lhv+nnlmdsh1Fh0KURGAzOi0tEXFexFb4+AisPwIkCCKwHN4XCDS3Axy1aviIiItVzsgBWHDi3PHUrjAqHmKC604Z2i4/8+fPns2rVKhISEujcuTMAFouFiIgIgoKCaN26tUPjadG/G71efohNT77O0bjdmOt706BlY4fGIBW1uPslmo982tlhSB3wcZo9uXW+5WnwURr8sTPc3dY5cXmab47C81ugxHqubNVB+OIgjGljP9d15cNaRC5NZiE8FgeJWfbhC1bsv7eegneS4D+9obmvk4MUERGpRfEn4I/xUFB6rmzdYVhzCG4JhWnRYKoDbWi3GMY4Z84cxo4dW5boAjCZTERERJT16iooKOCRRx6hXbt2XHXVVUyaNKnW4ol59i52vvoJRzbtwlZqpTgnn8zEg7W2PxFxHd8fhzm7wIb956yz+Zh/7oaNR50QmIdJzYLnfrQnuio7zx/sg2VpzohMRFzZc1sgOcv+99n3i7O/D+fBk9+D1VbZK0VERNzfkTz4ww9QWFq+/Oxn38qD8HaS4+NyBpdPdiUnJ5OWlsaoUaMqrDt48GBZsuvZZ5/Fx8eHpKQkdu3axUsvvVQr8Zjre9M4ui31mwQy8tt/cefOBQx89zn8QpvWyv5ExLUsTqn6jdP4ax2pnmVp9g/lqr6TLkqBUn1pFZFf7cm09+CyXmC9FdiXA3HHHRiUiIiIAy3fD0XWqtvQS/dVTIZ5Ipcfxnjo0CEAmjYtn0zau3cv6enpxMTEkJOTw+LFi/nll18w/DqmpVmzZpe8D39/f4qKiiqUtzU34tmAvuXK6gU2wGA0EnZzT9aNmUn+qTP0eHE8AxY+w8qhz1zu4dUpAwZYSC05fdF6vh160XZmbG2HU6kBAyzkJcY5Zd/i+ow+fnRecqrKOlYgIQN8G7egNLvqunJhUW8fwewfVGWdEwUQ3Lk3+albHRSV5+vyUQEA3t4+To7Es+k8145md82gychnMRhNF6xjKy3h3lmLOPTmow6MTERExDHav74H72ZtqqyTXQwtrruJnJ1fOSiqK2e1XugW1sW5fM+u4OBgAFJSznWVsNlsPPfcc1itVmJiYkhNTSU4OJgXXniB7t27Y7FY2LRpU63EU5xjb6D+vGAVOb+coDS/iG1/XUrwVRGat8uJOsyK1XxdUuuMPg0uva73pdeViozelzapjtHHr5YjERF3YfTxA9vFG8V63xAREU9lusTPuMv5XuOuXL5nV1RUFJGRkUyZMgUvLy/8/PyYN28e27dvx9fXlw4dOpCQkMC+ffuIiYnh5Zdf5ocffuCWW24hJSWFgICLP40vOzu70vJj8XtZPWJaubLi7DxyDh4Hm8bOXK4NG2Jp1qPjReslnIKJmx0QUCU2bIglOtg5+xbXV2KFQV9CbknV9XxMcPJAMj4X7lwgFzH6a/sTLy/2Trt701pCNNl0jen+60OFCwsLnRuIh9N5rh3L0uDlXVXXMZrM/HH8nTz69zsdE5SIiIgDTfgWdp2+8JD+s7774mPaNXRISNWyefNm+vbte/GKlXD5nl1ms5nly5cTEhLCuHHjmDx5MsOGDaN///507doVo9FI69atMZvNjBkzBoCePXvSuHFjkpJqZ+a1xMVriZp4M74tgjF5exHz7F2c3JFK7qGTtbI/EXENZiMMbw1VPbzECPwuFCW6qmlUeNWJLiNwXROU6BKRMsNaQr1LaNmOcOxDvEVERBzmtvCqE11GoFMgbpHoqi6XT3YBdOvWjbi4OPLy8khMTOSRRx5h+/btZZPTN27cmAEDBrBu3ToAkpKSOH78OJGRkbUSz665Kzj09XaGr/07o7fNx7dZEBsmvFwr+xIR1zIuEhr7VP7maQQCvWF8O0dH5XlGtIb2AZUnFo1APRM80cnRUYmIKwuoB49FVV3n3kho6fkjN0REpI4a2hKigypvQxsAowH+2NnRUTmHWyS7fis/P5/ExMSyZBfAf/7zH2bPns1VV13FXXfdxZIlSwgMDKydAGw2ts56jw+7TODDzuP5evwc9eoSqSMa+8DbfeHqSoa7dguyr2tW3/FxeZr6ZvhPbxgQUvHDuo0/vNkbOtSBO1IicnnuaQvPXwUBXuXLfU3waEd44iLJMBEREXfmZYTXroNhreyJrfO19oM3elNnpu1x+Tm7KrNz505KS0vLJbvatGlDbGys84ISh8hNiufgwj+AzYZ/14G0HDuz3Pri00fZNSmCLvOSqde4FQVHUjj45uNYiwtpNvwPBPYc7qTIxZOE+MJ/+kBaNuzIsJdd1QjaXnyKQLkMAfXg79fC0Xz48QQUW6F9Q+gcCIaqxpKKSJ12e4R9yPl3x+HpH+1la28AH7ds9YqIiFweXzO8eDU82Qm+Pw6FVmjrb78xX5fa0G75sd+zZ09smiC+TvJtE0PHOfbZ65OmD6I0LwuT77kMw/Ev/k2D9j3Llo98+AIRT3+I2S/Q0aFKHRDhb/+R2tW8PtyiOXZE5DLUM4El5NyyEl0iIlLXNPaB39XhNrRbDmOUustgto9LsJWW4hXUAqP3udmpS3IyKck+iXfTcACsxUUUnTzI/lfvI2XWCIozjzkjZBERERERERFxICW7xO1kfLOU3Y9HYWoQiMF07lbtiVVzaXLjw2XLJVknyU//ifDJi2h2y2SOLv+rM8IVEREREREREQdSskvcTlD/u+k8dy/FGYfJ378LAGthPgUHf8a3zbl53EwNGlI/7CrMfo3w69yPgkOJzgpZRERERERERBxEMxiIW7EWF2L08sZgNGKq74+hng8Ahcf3U3g0leQZN5KfvouSnNNETl2BsZ4P1uJCCg7uod6vwxtFRERERERExHMp2SVu5Uz85xxfNResVvw6X4+1IJdTG5YQPOBeOr78PQD7/3U/Le6xP6Wx2YinSJo+CIPRRPjv33Vi5CIiIiIiIiLiCEp2iVtp1Gc0jfqMLlfm2ya63PL5Sa2A6MEERA92QGQiIiIiIiIi4go0Z5eIiIiIiIiIiHgMJbtERERERERERMRjKNklIiIiIiIiIiIeQ3N2icuJDIAFfZy3bxERqVxsbCzjx48nLCyMsLAwFi1aVG79kiVLCAkJYfDgwfTr14/t27ezd+9eWrVqVa7e5MmTSUhIoLi4mNzcXBISErjrrrs4evQo2dnZhIaG8tlnnzF+/HjefvttDAaDIw/T6S7nPM+ePZvVq1cTHR3Nv//973L1xo0bR2pqKt7e3ixevJiWLVsydOhQ8vPzCQgIYNmyZfj4+PDwww+zYMECRx6iS6jN6xmgoKCAiIgIPv74Y/r27ctjjz3G3//+dxo0aOCoQxQREamz1LNLXI6fF0QHO+fHz8vZRy8i4tomTJhAbGwsDRs2JC4urty6lStXMmjQIAA+/vhjbr/99kq38eqrrxIbG8uf//xnbrrpJgA+/PBDYmNjmTRpUllZz5492bBhQy0ejeu6lPN87Ngxtm3bxrfffovZbGbLli3l6r3wwgts2rSJKVOmMHfuXADmz5/Pt99+y2233cbSpUsxm800adKE1NRUhx2bK6mt6xngnXfeoVOnTmXLt9xyC8uWLauFoxAREZHfUrJLRERELluXLl04dOhQ2XJGRgY+Pj5lvbCaN29+0W2sWLGCESNGlCtbuXIlt9xyCwADBgxg1apVNRi1+6nqPG/ZsoX+/fsDMHDgQOLj48u9Njw8HACTyVT271JZmcViYfXq1bV8JK6tpq/n0tJSNm3aRL9+/crWX3/99axZs6aGIxcREZHKKNklIiIily0uLo7IyMiy5ZSUFMLCwi5rG1u2bKFHjx5lyzk5OWRnZxMSEgJAWFgYSUlJNROwm6rqPGdmZhIQYB9/7+/vT2ZmZoXXW61WZs+ezYQJE8rK8vLyeOutt7jjjjsAnWeo+ev5ww8/rNATzNfXl9OnT1c/WBEREbkoJbtERETkki1cuBCLxULLli2Jjo6+4u1s3bqVmJiYcvNxrVmzhhtvvLEGonR/l3KeAwMDycrKAiA7O5vAwMAKdaZPn85dd91FmzZtysoefvhhZsyYQcOGDWsjdLdSG9ezzWbjs88+q9BrUURERBxHyS4RERG5ZGfnOJo5c2a58sjISNLT0y95O5UNYfxtWXp6Ou3atatewG7qUs7zNddcw8aNGwHYsGFDuV5yAJ988gkZGRk88MADZWX//Oc/6dSpE0OGDCkr03mu2es5JyeHffv2cdNNN/Hee+/x9NNPU1BQQF5eXqUJSREREal5SnaJiIhItQUFBZGXl1e2PHbsWNauXcuYMWP46quvAHjqqafK1n/99ddlk3+DfY6jpKSkchN6b9iwgWHDhjkgevdx/nlu3rw5Xbt2pV+/fhQWFtK9e3eOHj3Kyy+/DMAf//hHduzYgcViYdasWRQVFfGnP/2JL7/8EovFwsKFCwH7Uwl1nsurzvXs7+/P1q1b+fLLLxk7diz/+Mc/8PHxYePGjQwdOtSxByIiIlJHGWw2m83ZQbiqY/F7WT1imrPD8BjDVsykWY+Ozg5DREQuoPvn9t9bhl/Z6xcvXkyLFi0YPHhwjcQzfvx4Fi5ciNHoWffmXOk8l5SU8NBDD5UlvjxNdc51TV/Pjz32GHPmzMHPz69GticiIuLpNm/eTN++fdm0aRN9+vS5rNeaaykmERERqWPuu+++Gt3eO++8U6Pb8xQ1eZ7NZrPHJrqqq6av57lz59bo9kREROTCPOtWqYiIiIiIiIiI1GlKdomIiIiIiIiIiMdQsktERERERERERDyGkl0iIiIiIiIiIuIxlOwSERERERERERGPoacxisvJKYaULOfsOzIA/Lycs28RERERERERqT4lu8TlpGTBxM3O2feCPhAd7Jx9i4iIiIiIiEj1aRijiIiIiIiIiIh4DCW7RERERERERETEYyjZJSIiIiIiIiIiHkPJLhERERERERER8RiaoF48QuJUC7l74zCYvTCYzPi07EiLsbMI6DbI2aGJiIiIiIiIiAOpZ5d4jBZjZxGzLIeui47RoEMvUmffSmnuGWeHJSIiIiIiIiIOpGSXeByjlzeNh0zAWpBDwZEUZ4cjIiIiIiIiIg6kZJd4HGthPifXLcDoG4BPi3bODkdEREREREREHMhtkl1FRUVMnz6d0NBQ6tevz8CBA4mPj8dgMLBixQqHxXFPypJyP/cd+JDhX73isP3LhR1eOp2EuwPZ9WA4eWk7iJz2BSbfAGeHJSLi8gpK4LP0c8uzdsDu086Lx1OVWGH94XPL07dB/Amw2ZwXkyey2iDuOEzdeq5swxH7+RcREZG6wS0mqLfZbIwePZr4+HhmzJhBZGQkS5cuZeTIkQDExMQ4LJb3I+8ttzz8q1dIW7HZYfuXC2tx90s0H/m0s8MQEXErKVnwWBycKjxXtiId/psOw0NhajSYDE4Lz2Mcz7ef57Scc2VrDsHqX+C6JvDytVDfLVplri2nGP7wA2zPAON51+0zP0K7AHj9Ogj2cV58IiIi4hhu0ayaP38+q1atIiEhgc6dOwNgsViIiIggKCiI1q1bOyWuxtGRBLZvRcqyDU7Zv4iISHVkF8Oj30FmUfnysx1gPj9oTww8FuXw0DxKqQ0e/x7Sc8qXW3/t0fXDCXgxAf7a3eGheZxp2yAhw/639Tc95lKz4A/xsKgfGJTAFRER8WhukeyaM2cOY8eOLUt0AZhMJiIiIvDy8mL//v3ceuutZesyMzPJysoiIyOjVuNqd/dADn29nfxjGushIiLu54uDkFFUdZ0P9sH97aCBW7QYXNPmY7Av+8LrbdiHNz6WC60aOCwsj5OSBZuOXXi9Ffg5E7achGubOCoqERERcQaXb7omJyeTlpbGa6+9VmHdwYMHGTVqFOHh4SQkJJSVT548mZKSkkveh7+/P0VFFVv7bc2NeDagb6WvMdf3JmJEH7598vVL3k9dN2CAhdSSiycGfTv0ou3M2NoOp1IDBljIS4xzyr5FRBytzaxv8I3sgcF44Sk8C0ohfNBdZH3/XwdG5llaPfE2gX3uxGC6cLPLarPR8/6pnPxc84BeqaZ3TKfpqClVXs+20hLueOEdDr/1uAMjExERkSthtV75hJsun+w6dOgQAE2bNi1XvnfvXtLT0yvM11VUVMT777/PmjVrajWu8Ft6UZJfxC/rt168stS6DrNinR2CiIjbMTVoVGVioKyeb0MHROO5TL4NLz5uzlqKqYEeqlIdJt+GYCul6ucv2fTwGhERkTrA5ZNdwcHBAKSkpNCjRw/APmH9c889h9VqrZDs+vzzz2nZsiVXX331Je8jO7vysQXH4veyesS0Ste1u2cQqR/FYivVo30u1YYNsTTr0fGi9RJOwUQnzfm/YUMs0cHO2beIiKM9+T18f/zcHF0XsmLxm/Rs8qZDYvJEf98Fy9OqPs8Gk5lXZzzPbe8+77C4PM37qfB/u6uuYzR58fv77+SJv9/pmKBERETkimVlZbFr1y6uuuqqy37txW/nOllUVBSRkZFMmTKFjz/+mNWrVzNixAi2bduGr68vHTp0KFf/7bff5oEHHqjVmALatqBp9w4kffBVre5HRESkNt3a+iIJGKCZD3Rv7KiIPNOIi5xngHpGGNrSIeF4rGGtwHyRDnQ24BbnPNdIRERELlNAQAB9+vQhIODye2W7fLLLbDazfPlyQkJCGDduHJMnT2bYsGH079+frl27Yjxv+MWhQ4f45ptvuOeee2o1pnZjBnLshz1kpx2t1f2IiIjUpuubQ0yQPan1WwbsiYE/dAGTnlxXLR0aws2tqq7zcEfw83JMPJ4qyBseaF91nVFhEO7nmHhERETEeVx+GCNAt27diIsrP2n466+/Tv/+/cuVLVq0iJtvvrls6GNt2TrzvVrdvoiIiCOYjfDqdfBSAnx12J7cOivAC565Cga3cFZ0nmV6tD2Z9cl+KDnvRNc3wUMd4Z42zorMszzY3t676+1k+8MVzjIbYEwbeLyT82ITERERx3GLZNdv5efnk5iYyOTJk8uVv/vuu5U+tVE8R0l2Bsl/GULBoURiluWUW3dm25cc+fAFjPX9CX/yXeoF27+hFZ8+yq5JEXSZl0y9xhe5tS4iUsc0MMPfusPhPNh41J4gCG1g7/Xl5fL9v92H2WhPHk5oDxuOQFYxNPWBASHg65atMddkMNh7d90RYT/PJwshsJ79PAfWc3Z0IiIi4ihu2bzauXMnpaWlFSanT0pKclJE4iim+v60e2Ed+/5+R4V1Rz+ZQ/uZGyg8ksKxT+cQ+uC/ADj+xb9p0L6no0MVEXErLXzhLvUuqnVB3jAq3NlReD4/L83NJSIiUpe5ZbKrZ8+e2Gy2i1cUj2Mwe2H2D6p8ncmMsZ4PPq07k5scD0BJTiYl2SfxbhruwChFRERERERExFk0QEE8hq20mJKsU+T8vImS7AwATqyaS5MbH3ZyZCIiIiIiIiLiKEp2icdoec9MUufczulvP6B+aCeshfkUHPwZ3zYxF3+xiIiIiIiIiHgEtxzGKFIZv0596TBrA7lJ8WTv/JrC4/spPJpK8owbyU/fRUnOaSKnrnB2mCIiIiIiIiJSi5TsEreTNH0weWnbSZo+mBZjZpCzN47mtz3D4Q9fJOenWLyCWxL26JsYvX3p+PL3AOz/1/20uGemkyMXERERERERkdqmZJe4nfYvrS+37NepLwAt7voz8OdKXxP++3drOSoRERERERERcQWas0tERERERERERDyGkl0iIv/f3p3HR1Xf+x9/TRY2WYMCLgECQYwgkCqgBmQA6xWtKCqtC5UqVq1VS7UWq3IvWpfLtd56XVvqjsX+KtWiLVVECYKiEVlFCAQhAgqyCIQtkMz8/pgSjOyQzGQOr+fjkQdzvufMzOeMx5lz3vP9fkeSJEmSFBiGXZIkSZIkSQqMUDQajSa6iJpqe8kWvpn/RaLLCIwmOS2p1aDefrfbtAOKNsahoD3Ibgj10xPz3JIkSZIk6fAZdkmSJEmSJCkwHMYoSZIkSZKkwDDskiRJkiRJUmAYdkmSJEmSJCkwDLskSZIkSZIUGIZdkiRJkiRJCgzDLkmSJEmSJAWGYZckSZIkSZICw7BLkiRJkiRJgWHYJUmSJEmSpMAw7JIkSZIkSVJgGHZJkiRJkiQpMAy7JEmSJEmSFBiGXZIkSZIkSQoMwy5JkiRJkiQFhmGXJEmSJEmSAsOwS5IkSZIkSYFh2CVJkiRJkqTAMOySJEmSJElSYBh2SZIkSZIkKTAMuyRJkiRJkhQYhl2SJEmSJEkKDMMuSZIkSZIkBYZhlyRJkiRJkgLDsEuSJEmSJEmBYdglSZIkSZKkwDDskiRJkiRJUmAYdkmSJEmSJCkwDLskSZIkSZIUGIZdkiRJkiRJCgzDLkmSJEmSJAWGYZckSZIkSZICw7BLkiRJkiRJgWHYJUmSJEmSpMAw7JIkSZIkSVJgGHZJkiRJkiQpMAy7JEmSJEmSFBiGXZIkSZIkSQoMwy5JkiRJkiQFhmGXJEmSJEmSAsOwS5IkSZIkSYFh2CVJkiRJkqTAMOySJEmSJElSYBh2SZIkSZIkKTAMuyRJkiRJkhQYhl2SJEmSJEkKDMMuSZIkSZIkBYZhlyRJkiRJkgLDsEuSJEmSJEmBYdglSZIkSZKkwDDskiRJkiRJUmAYdkmSJEmSJCkwDLskSZIkSZIUGIZdkiRJkiRJCgzDLkmSJEmSJAWGYZckSZIkSZICw7BLkiRJkiRJgWHYJUmSJEmSpMBIS3QBkiRJkiTpyLW9ZAvfzP8i0WUERpOcltRqUC/RZSSUYZckSZIkSUqYb+Z/wb8uvDvRZQRGv3H30bzbSYkuI6EcxihJkiRJkqTAMOySJEmSJElSYBh2SZIkSZIkKTAMuyRJkiRJkhQYhl2SJEmSJEkKDMMuSZIkSZIkBYZhlyRJkiRJkgLDsEuSJEmSJEmBkTRh1/bt2xk+fDiZmZnUrVuXPn36UFBQQCgUYty4cYkuTwG1YTuMWQy/nQUj58DUVVAeTXRVkqRoFGavg4c/hXtnwdOFsHJroquSJElVKZSaQrffXs3lnz3HFQte4MyHf0Zq7fREl6UkkJboAg5ENBpl4MCBFBQUMGLECLKzsxkzZgwDBgwAIDc3N8EVKoheXQoPfQo7IpAagijwylLIPAp+3x1a109wgZJ0hFpfCr/6GGati31rFwpBJAp/LIQfZ8NNOZASSnSVkiTpcHW65WJanNmRcX1uo3xHGX2fH8apd/+YguHPJro01XBJ0bNr1KhRjB8/nokTJ3L99dfTt29fRo0aRWpqKhkZGbRs2TLRJSpg3l4BD8yJBV0Q680V+XePrhVb4Ib3YxdbkqT4KovATR/CnHWx5Qix9+gosb8Xi+DphQksUJIkVZl2V/Zl7qOvsmXlOkrXbmTWw38l+0dhQilJEWUogZLiCBk5ciSDBg2iQ4cOFW2pqalkZWVV9OpauHAhZ5xxBieeeCJnnHEGixYtSlS5SnLRKDy5APbWKSAShTWl8NoXcS1LkkRsOPmCDbGQa29eWASbdsStJEmSVA1qNaxH/eOPYd28JRVta+cuoVaDetTPPCaBlSkZ1PhhjIsWLWLJkiU8+uiju61btmwZl1xyCQA33HADP//5zxk0aBAvvfQS119/Pe++++5+H3/jxo20aNGCsrIyQiHHPAjqtj2V7P/+YJ/bRKMR/vedxdzQsWOcqpIkAbS87S807HoBodS9n8KURqDt+UNYP/mlOFYmSZIOVdu0Jvy6YY9KbWn16wKwfcOWirbtGzYDkP7vddqz3r3DLC77JtFlVIlatWpRUlJy0Per8T27VqxYAUCzZs0qtS9YsIDi4mJyc3P5+uuvmTFjBpdffjkAl19+OTNmzGD16tX7ffy5c+eydetWolFnHVdMWqNm+90mFEo5oO0kSVUrrXHzfQZdFdv5Hi1JUlIr2xT75Zn0hvUq2mo1OgqAHZv8VRrtW43v2dW0aVMAioqK6NatGxCbsH7YsGFEIhFyc3NZtmwZxx9/PKmpqUBsiONxxx3HsmXLOOaYA+vemJ+fT15eXvXshJLKvG9g8JT9b5fVrBEzS524S5Li6faPYfJX+x7GCPDH/32QH7zyYFxqkiRJh2dVwQL+deHdldq2b9zCphWryejQmo2LvwSgaccstpdsYdOy/XdsOZJNmpRP824nJbqMhKrxYVdOTg7Z2dnceeedpKenU79+fZ566ilmzpxJvXr1aN++PTNnzkx0mQqQkxtDy6Ng2ebYZMd709/fRZCkuDv/BJj01b63qZMK4RbxqUeSJFWfRX9+h043D2DVR/OJlJXT5bYfUvT/8olG9ve1l450NT7sSktLY+zYsdxwww0MHjyYzMxMhg4dSsOGDVm8eDEpKSlkZmayYsUKysvLSU1Npby8nC+//JLMzMxEl68kFArBjTlwx/Q9r08BmtaBi1rFtSxJEtCjeexLiQXr99676yfZUD89jkVJkqRqMefRV6md0ZCL8n9PKCXE0n98yCf3Oyen9q/Gh10AnTt3Ztq0aZXaHn/8cXr16gXE5vPq0qULL7/8MoMGDeLll18mNzf3gIcwSt919nFwd2f4n7mw/TtXUyccBY90h8a1ElObJB3J0lLg0dPh9gKYuW5Xe+jff1dlw5ATE1WdJEmqStHyCAXDn6Vg+LOJLkVJJinCru/aunUrhYWFDB06tKLtD3/4A4MHD+bee++lSZMmvPjii4krUIFwUSvocyyMXw6/+zTW9ujpcPoxkOIPd0pSwjSuBaPyYN56+Mm/51j82Unwg0xo5o8zSZIkHfGSMuyaM2cO5eXl5ObmVrSddNJJfPTRRwmsSkHUsBZc1mZX2HWmP+4lSTVCKAQdm+xavsbeXJIkSfq3pAy7unfvTjS6r6nDJUmSJEmSdCRKSXQBkiRJkiRJUlUx7JIkSZIkSVJgGHZJkiRJkiQpMAy7JEmSJEmSFBiGXZIkSZIkSQoMwy5JkiRJkiQFhmGXJEmSJEmSAsOwS5IkSZIkSYFh2CVJkiRJkqTAMOySJEmSJElSYBh2SZIkSZIkKTAMuyRJkiRJkhQYhl2SJEmSJEkKDMMuSZIkSZIkBYZhlyRJkiRJkgLDsEuSJEmSJEmBYdglSZIkSZKkwDDskiRJkiRJUmAYdkmSJEmSJCkwDLskSZIkSZIUGIZdkiRJkiRJCgzDLkmSJEmSJAWGYZckSZIkSZICw7BLkiRJkiRJgWHYJUmSJEmSpMAw7JIkSZIkSVJgGHZJkiRJkiQpMAy7JEmSJEmSFBiGXZIkSZIkSQoMwy5JkiRJkiQFhmGXJEmSJEmSAsOwS5IkSZIkSYGRNGHX9u3bGT58OJmZmdStW5c+ffpQUFBAKBRi3LhxiS5Pkmq8SBQ+/Boe/wz+bx68vQJ2RBJdlSQJ4JtSePlzeGQePL0QijcluiJJSrzWF5xBv3G/5cqi0Vxa8GSiy1ESSUt0AQciGo0ycOBACgoKGDFiBNnZ2YwZM4YBAwYAkJubm+AKJalmW7wRbv8Yvti861uOCNCkFtx3KnQ/JpHVSdKRKxqFUYXw3CIoi0JqKPblxB8WQN9jYUQu1E2KM3ZJqnqlGzaz4Nk3qXNMIzpc94NEl6MkkhQfnaNGjWL8+PHMmjWLDh06ABAOh8nKyiIjI4OWLVsmuEJJqrlWboXr3oeSHbHlb3fm2rAdfvEhPN0DOjZJSHmSdER7ZiH8aeGu5fLortvvfgVbyuDR0yEUin9tkpRoX703B4CW53ZNcCVKNkkxjHHkyJEMGjSoIugCSE1NJSsrq6JX169+9SuysrIIhUJ8+umniSpVkmqclxfDxh2VQ66dIsR6EPxxQbyrkiSV7IBnF+19fRSYthpmrotbSZIkBUKND7sWLVrEkiVLuOSSS3Zbt2zZsoqw66KLLuK9996jVatW8S5RkmqsaBTGfRG7YNqbCPDhalizLV5VSZIAJn4J2/czd2JKCN74Ij71SJIUFDV+GOOKFSsAaNasWaX2BQsWUFxcXBF29ejR47CeJxwOk5JS47M/JUjHv8ZSgNq16yS4EunghNJq0fHlkv1uFwXadu7GtqWzq78oqQr5/qxk1uzSu2g28G5C+zgHLY9EGfPGRB443blqJAVX27Qm/Lrh4V3Ta5fevcMsLvsm0WVUiVq1alFSsv/rme+q8elO06ZNASgqKqpoi0ajDBs2jEgk4uT0krQP0bLtREq3HNC25ZscJyNJ8VS+ad0+gy4AIuWUb1obn4IkSQqIGt+zKycnh+zsbO68807S09OpX78+Tz31FDNnzqRevXq0b9++Sp4nPz+fvLy8KnksBc9pr8f+LS0tTWwh0iG4fzaMK97znF0Q+9ajYxOYvqJoL1tINZfvz0pma7bBeW/H5k7cm1BqGs/fdhk9/uey+BUmSXG2qmAB/7rw7t3aQykppKSnEkpLg1CI1NrpRKNRItvLElBl8pg0KZ/m3U5KdBkJVePDrrS0NMaOHcsNN9zA4MGDyczMZOjQoTRs2JDFixc79FCS9uPKNjB+GeyI7DnwigI/rZrvDSRJB+HoOnBpa/jrkj2vTwHaNYIzmu15vSQFXdtLz6LH/91UsfzjpS+zadnXjO12YwKrUjKo8WEXQOfOnZk2bVqltscff5xevXolqCJJSh6tG8R+tv62Atj0nS/B0kJwdxcvpCQpUW7tAJt3wD+Xx8Ktb38pcVJjeKQ7pIYSVJwkJVjRX/Mp+mt+ostQEkrKblFbt26lsLCw0nxdt9xyCyeccALLly/n7LPPpkOHDgmsUJJqllOPhn+dA3d33tV2y8nw5jnwg8zE1SVJR7q0FLjne/CXMFzWZlf7E2fA8z0ho3bCSpMkKWklZdg1Z84cysvLK4Vdjz76KMuXL6esrIyVK1cyb968BFYoSTVP3TS4qNWu5auyobEXUZJUI2Q3hFs77lrufgyk2KNLkqRDkhTDGL+re/fuRKP7mMlTkiRJkiRJR6Sk7NklSZIkSZIk7YlhlyRJkiRJkgLDsEuSJEmSJEmBYdglSZIkSZKkwDDskiRJkiRJUmAYdkmSJEmSJCkwDLskSZIkSZIUGIZdkiRJkiRJCgzDLkmSJEmSJAWGYZckSZIkSZICw7BLkiRJkiRJgWHYJUmSJEmSpMAw7JIkSZIkSVJgGHZJkiRJkiQpMAy7JEmSJEmSFBiGXZIkSZIkSQoMwy5JkiRJkiQFhmGXJEmSJEmSAsOwS5IkSZIkSYFh2CVJkiRJkqTAMOySJEmSJElSYBh2SZIkSZIkKTAMuyRJkiRJkhQYhl2SJEmSJEkKDMMuSZIkSZIkBYZhlyRJkiRJkgLDsEuSJEmSJEmBYdglSZIkSZKkwDDskiRJkiRJUmAYdkmSJEmSJCkwDLskSZIkSZIUGIZdkiRJkiRJCoykCbu2b9/O8OHDyczMpG7duvTp04eCggJCoRDjxo1LdHmSDkM0Ch+vhucXwQuL4NNvYm1Sslq6CV5aDM8uhPyvoCyS6IokSYqf8ii8vwqeWwSji6BwQ6IrUjJKqZXGmb+7gUs+fIIrF41mwNRHOemafokuS0kiLdEFHIhoNMrAgQMpKChgxIgRZGdnM2bMGAYMGABAbm5ugiuUdKjmr4e7PoEvNldub98IHjwVWtZPSFnSIVm/Hf5zBnzwdeX2jFpwVxfo1SIhZUmSFDcz1sY+C1durdzeJQPuPxWa101MXUo+KampbP16PRMu+y0lxavIOLkV33/5bratXs/SN6YlujzVcEnRs2vUqFGMHz+eiRMncv3119O3b19GjRpFamoqGRkZtGzZMtElSjoEn5fAde/D8s27r1u0Ea6dCl9v3X2dVBNtK4cbP4APv9593Tfb4VcFu4dgkiQFyaffwM+n7fn8bc662Lndhu3xr0vJqWxrKTP/5y+ULF0J0Sjr5i1l2YTpNOuWk+jSlASSIuwaOXIkgwYNokOHDhVtqampZGVlkZuby9q1aznvvPNo3749p5xyChdffDGrV69OYMWSDsSfCqG0HPY0wisSjQUEf14c97KkQ/Lmcli4cc/H885RuY/Mc4iuJCm4npwP5ZG9nNsBX22FsUvjXJQCI5SWSvPuOXwzvzjRpSgJ1Piwa9GiRSxZsoRLLrlkt3XLli0jNzeXUCjEr3/9awoLC5k7dy5t27bljjvuSEC1kg7Uxu3wzld7PhnaKQr8/YtY8CXVdK8VQ2gf66PEejPOd94SSVIArdwCBWv2fW4Hsc9L6VCc/sAQdmzayuJXJie6FCWBGj9n14oVKwBo1qxZpfYFCxZQXFxMbm4uGRkZhMPhinWnn346Tz311EE9TzgcJiWlxmd/SpCOf90GQO3adRJcSXDUPv4kTnxk9n6321wGR2UcQ2TLxjhUdWTweK4eOX9aRlrjZvvdrlf/H7Hxo79Xf0FHCI9nBY3HtJJVvfZn0Pa+/P1u99XmcmrXrlf9BSmptE1rwq8b9tjr+q4jBnPMqe15a+AIIjvK4lhZcurdO8zism8SXUaVqFWrFiUlJQd9vxqf7jRt2hSAoqKiirZoNMqwYcOIRCK7TU4fiUR46qmn6N+/f1zrlHRwyrccWPeWaHkZkdIt1VyNdPjKt6wnegBjFMs327VLkhQ8B3puF9l68BetOrJ1u/cnHHdWJ9764T2UrvP40YGp8T27cnJyyM7O5s477yQ9PZ369evz1FNPMXPmTOrVq0f79u0rbX/zzTdTv359brrppoN6nvz8fPLy8qqydAXIaa/H/i0tLU1sIQFzzZTYRKZ76+6eAvTNTOOTLXuYwV6HzOO5eowqjP3tS5NasOrjN0mv8V81JQ+PZwWNx7SSVTQKl06CLzbtmqvyu1KASzs05g6Pb33HqoIF/OvCu3dr7/bbazi2R0fevHQEpWsd6XGgJk3Kp3m3kxJdRkLV+LArLS2NsWPHcsMNNzB48GAyMzMZOnQoDRs2ZPHixZWGHv7qV79i0aJFvPHGGw5JlJLANSfC0I/2vC4EhEIwqG1cS5IO2cWt4OXPYfOOvQe4g9th0CVJCqRQCK49EYbP2Mt6IC0FLsuKa1lKYkedcDQnX3se5du2c+lHT1S0r/poAROvvD+BlSkZ1PiwC6Bz585MmzatUtvjjz9Or169KpbvvPNOPvnkE/75z39Su3bteJco6RD0aA6/6QQj58S+Afz2t4BpKXD/96BDk0RVJx2co+vA46fDLR/Chh272lOIhV+D2sKVbRJVnSRJ1a/fCbBqKzw+PxZuffvcrk4qPNQVWjdIVHVKNpuXr+H5Yy9NdBlKUkn5/fLWrVspLCysmK9r3rx5PPjgg3z55ZeceeaZdOnShQEDBiS4SkkH4pLW8PrZsV5eO92UA//8PvQ5LmFlSYekQxN4/fuxEHengVnwlzAM7RD71luSpCD7STt4rU/l3vm3doid252+/99xkaQqkRQ9u75rzpw5lJeXV4RdHTp0OKBJgSXVTC3qwc9OgmcWxpZ/0i6x9UiH46i0WIj74JzY8u2nJLQcSZLiLrM+/KIDjF4cW77CaSkkxVlShl3du3c33JIkSZIkSdJuknIYoyRJkiRJkrQnhl2SJEmSJEkKDMMuSZIkSZIkBYZhlyRJkiRJkgLDsEuSJEmSJEmBYdglSZIkSZKkwDDskiRJkiRJUmAYdkmSJEmSJCkwDLskSZIkSZIUGIZdkiRJkiRJCgzDLkmSJEmSJAWGYZckSZIkSZICw7BLkiRJkiRJgWHYJUmSJEmSpMAw7JIkSZIkSVJgGHZJkiRJkiQpMAy7JEmSJEmSFBiGXZIkSZIkSQoMwy5JkiRJkiQFhmGXJEmSJEmSAsOwS5IkSZIkSYFh2CVJkiRJkqTAMOySJEmSJElSYBh2SZIkSZIkKTAMuyRJkiRJkhQYhl2SJEmSJEkKDMMuSZIkSZIkBYZhlyRJkiRJkgLDsEuSJEmSJEmBYdglSZIkSZKkwDDskiRJkiRJUmAYdkmSJEmSJCkwkibs2r59O8OHDyczM5O6devSp08fCgoKCIVCjBs3LtHlxV15FD74Gl5ZAv9cBuu3J7oiSZIURNvKYOKXsXOOd7+E0vJEVyQdukgUClbHjud/fAFrtyW6IunwLN4Iry6N/RVtTHQ1Ve/0B69l4PQ/cMXCFxk44490u/cnpKSnJbosJYGkOEqi0SgDBw6koKCAESNGkJ2dzZgxYxgwYAAAubm5Ca4wvt79Eh76FFZ/68M5LQQXt4ZfdoD0pIkwJUlSTRWNwgtF8Nwi2Fy2q71+GlzXHi5vA6FQ4uqTDtYHX8ODs+GrrbvaUkNw/gnw61OgTlJcGUkxKzbDiJkwc13l9i4ZMCIXTjgqMXVVtfnPvcn0e0dTtrWU2hkNCI+6jU63XMysh/+a6NJUwyXFW/qoUaMYP348s2bNokOHDgCEw2GysrLIyMigZcuWCa4wft75Eu6Yvnt7WRT+ugS+3goPdfXkU5IkHZ6nFsCzi3Zv31QG/zsPtpXDNSfGvy7pUHz4NQz9KBbiflt5FN5YFgvAHjsd0vzSWElg9TYYMhXWle6+bs46uHoKjO4FLerGv7aqtmHh8l0LoRDRSJQGbY5NXEFKGknxdj5y5EgGDRpUEXQBpKamkpWVVdGr66KLLqJz587k5ubSs2dPZs2alaBqq09ZBH43N3Y7updt8lfC9DVxK0mSJAXQyq2xHl37MqpwzxdaUk0TjcZGRUSjez6HjgIfr4H3Vsa7MunQvLAo9v4b2cO6CLBhOzy/n/fwZHLKTRdxZdFoLv/0WTI6tOKzUf9IdElKAjU+7Fq0aBFLlizhkksu2W3dsmXLKsKuF154gdmzZzNz5kx+9atfcc0118S71Gr34WpYXbr3oAsgJQSvFcetJEmSFEBvfLH/bcqiMH5Z9dciHa4530Dxpv2cQwOveg6tJLAjAq9/seega6cI8I9lsR64QTD38b/z5+wf89pZv6DwxQls/fqbRJekJFDjhzGuWLECgGbNmlVqX7BgAcXFxRVhV6NGjSrWbdiwgZSUg8vxwuHwQd8n3pqeeyPHDfn9PreJROHvU2bwcN4ZcarqyNDxr7EJ0mrXrpPgSoLN1zk+fJ3jw9c5Pnydq8cJP3+axj0vI5SavtdtomU7GP77Zxny9C1xrCz4PKarXuOzriDz5uf2uU0EeO/TJdQOnxSfoo4QHs9VL61xC3L+tP9kdls5HN0ymx1rkuNbibZpTfh1wx773GbDohWsm1dMz8du4a1LR8SnsCTVu3eYxWXBCAVr1apFSUnJQd+vxoddTZs2BaCoqIhu3boBsQnrhw0bRiQSqTQ5/bXXXsuECROIRqO8+eabCam3OpVv2/9/4GiknMjWgz8QJEmSdops2wTsZwLQlJR/byfVbJGt+z9Oo9EI5Z5DKwlESjcf+LbbDnzbZJGSnkpD5+zSAajxYVdOTg7Z2dnceeedpKenU79+fZ566ilmzpxJvXr1aN++fcW2Tz/9NACjR4/m9ttvZ/z48Qf8PPn5+eTl5VV5/VVpXSmcNyE2bGBvQimp3Ht5L354p5NoVKXTXo/9W1rq61qdfJ3jw9c5Pnyd48PXuXp8vBp+Nm3f24RSUvnn726j4zO3xaeoI4THdNXbXAb/8da+h3SFQincem4nhtzi616VPJ6rx40fxOZp3ttQxpQQdG4C09d+Fde6DseqggX868K7K7WlN6hHq37d+OLNArZv3EKTnFZ0HnoJX+bPTlCVyWPSpHyadzuye6rW7HF7QFpaGmPHjuXYY49l8ODBDB06lH79+tGrVy86deq0x6GHP/7xj5k0aRJr165NQMXVJ6M29N/HD0+mhKBJLTjvhPjVJEmSgue0o6F9o72fKKYQ+3n7Do3jWJR0iI5Kgx9l7X19yr+3uejI+YF3JbmrsvczZ1cUBreLWznVJxqlzSVnccmHT3Bl0Wj6PPdrlr8zk4/ueibRlSkJ1PieXQCdO3dm2rTKXy8+/vjj9OrVC4BNmzbxzTffkJmZCcAbb7xBRkYGGRkZca+1ut3WEb7eBlNXxcKtyLd6eTVMh8fPgPp7n15DkiRpv0IheKQ7/OwDWPqtEWApxC6w2jaE/+ka205KBj87Cb7aChNW7H4OXTcNHj0dmjqtlJLE6c3gjk4wck5seefhvPMt+baO0KN5IiqrWjs2bWXCj+5NdBlKUkkRdn3X1q1bKSwsZOjQoQBs3ryZgQMHsnnzZlJTU8nIyOCNN94gFMAzsNqp8L/doGB17Bdj3v13z9RfdYTzM6GBQZckSaoCx9SBP/eCd76E/5wZa+t+DPygJfQ5FtJr/PgAaZe0FLj/e3Bxq9gvl78V+w0sfnEyXNASGtdKbH3Swbq0NXQ9Gv62FMZ8Hmu7rA1c0gpaN0hkZVLNkJRh15w5cygvL6+YnL558+Z8+OGHCa4qflJCsTT/9Ga7xsFf1iaxNUmSpOCpnQrnZe4Kux7zx56VxEKh2BDd047eFXb9ODuxNUmHo1V9uLXjrrDrto6JrUeqSZIy7OrevTvR6D5maZckSZIkSdIRyQ7okiRJkiRJCgzDLkmSJEmSJAWGYZckSZIkSZICw7BLkiRJkiRJgWHYJUmSJEmSpMAw7JIkSZIkSVJgGHZJkiRJkiQpMAy7JEmSJEmSFBiGXZIkSZIkSQoMwy5JSSE/P5+srCzC4TCDBw/ebf3o0aOZOHEiAA888AA9e/bk5ptv3m271atXM2DAAHr37s2oUaMAePbZZznzzDO56KKLKCkpYceOHeTl5dGrVy/69+/Ptm3bWLduHbfeemv17qQkSZKkuOjxyM/pfv+QRJexR1cWjSajY1aiy0hqhl2SksaQIUPIz8+nUaNGTJs2rdK6N954g759+7Jq1SpmzJjBlClTSEtLY/r06ZW2u/fee3nkkUeYNGkS1113HWVlZbzwwgtMnTqVa6+9lqeffpr09HQmT57M5MmT6dq1KxMmTCAjI4P169ezadOmeO6yJEmSpP1ocWYHzv3bPVy5aDRXLhrNuX+7hxZndEh0WRW63PZD+r74mwPe/s/ZP2bdp0uqsaLgM+ySlHQ6duzIihUrKpbXrVtHnTp1CIVCTJ8+nV69egHQp08fCgoKKt134cKF3HHHHZxzzjkUFhayZs0aWrZsSUpKCqecckpFiJaWlgZAJBKhbdu2AHTv3p38/Pw47KEkJZf99b69//77WbRo0T4f4+abb6Znz5488MADu60rKCggLy+PM888k7vvvnu35Z3++Mc/0rdvX8LhMDt27GD+/PmMHDny8HdQklRjnXD2qXx/zN0sm/gJf/3e9fz1e9ezbOInfP/luzmh7/f2eJ9QatVFIaG01Cp7LFUdwy5JSWfatGlkZ2dXLBcVFdGqVSsA1q9fT8OGDQFo0KAB69evr3TfqVOnct999/HEE09w1113ccwxx1BUVMS2bdt477332LBhAwCfffYZXbt25e233yYzMxOAVq1asXDhwjjsoSQln731vo1EIsyZM4d27drt9b7Tp08nLS2NKVOmMGPGDFatWlVpfW5uLu+//z4ffPAB06ZNo3379pWWN27cyBdffMHcuXN55513yM/PJz09nZycHGbMmEE0Gq22/ZYkJVb3+66h6JV85j31OjtKtrCjZAvznnqdxWMn0+2+awC4tOBJOg29hPNev59Bn/+ZozvHvsxOP6oO4VG3ceWi0Vw0+fccF+683+drcUYHriwazYmDzubSj5+i/8TfAXDsWZ34wVsjuaLwBfq/8zAtz+0KQMtzu3LKLQM4vncXriwazZVFo0mplbbP5/jJV2Np2rktodQUrlj4Io3bx65HWpzZgZ98NZZWPzgDgLrNGnPVF38hvX7dQ3vxAsywS1LSeOaZZwiHwxx//PF06dJlj9s0btyYjRs3AlBSUkLjxo0rre/QoQNt27alXbt2bNiwgdTUVG6//XbOPfdcZs+eTfPmzQE4+eST+fjjjxk4cCAvvPBCde6WJAXKd3vfzp49mxNPPHGf9/noo4/o06cPAL169eKTTz6ptD49PR2A8vJyjjvuOI466qhKy/Xq1WPChAls3ryZPn36MGLEiIr7tmnThnnz5lXFrkmSapiGbY+jQavmLPn7+7ut+/y1qTRs3YKGbY4FIHtgmPdve5I/Z/+Ytf8eIph1UQ8+f20KY04azJzHXqPPs7+mXouM/T5vWt3aHN0lm7+fNZR/9BtGg9YtOPuFO/j08b/z8slX88n9L9HrqV+S0aE1X7z5MXMffY0Vk2bx5+wf8+fsHxPZXnZA+xctj7Dqo/kc2+MUAI7teQobl3zFsT06xpZ7nMLaOZ+zY9PWA3q8I4lhl6SksbPXwH333VepPTs7m+LiYgBOPfVU3nvvPQAmTZpEt27dKm3bunVr1qxZw6pVq6hXrx4AF198Mfn5+XTv3p1+/fqxY8eOil4AjRo1ok6dOgAUFxfvs2eCJGn33reLFi2idevW+7zP/nrlAowZM4acnBwaN25MWlrabstff/01oVCId999lyVLljBr1izAXrmSFGR1MhoAsHXVut3Wbf36m9g2TWOfL4Wj32bDohVEI5GKsGnlh5/xxb8KiJZH+Hzse3zzWTGtLzhjv88bSknhkwf+TNnWUsq3bifrwjNZ9dF8lr4xjWh5hBXvzuSLCdNpO7DXYe/jyqmfVgq3Zv3urxXhV4u8jnz1/qeH/RxBZNglKellZGSwZcsWAFq0aEGnTp3o2bMnpaWlnHbaaaxcuZKHHnoIgLvvvpuLL76YAQMGMHz4cABuvPFG+vbty3vvvccPf/hDiouLCYfD9O7dm9dff50rr7wSgA8//JBwOJyQfZSkmu5Aet9+9tlnhMPhSn///d//vd9euQBXXHEFCxYs4Msvv2Tu3Lm7LTdq1KhizsazzjqLwsLC6tpVSVINsW1dCQB1m+/eG6tusyaxbdbGPl82r1i92zabl1du27Rs9QH17CrbWkrpv58boN6xTdn03ccqXkW9Y5vu97H256upc2l++snUaliPhm2OY8nf3ye1Vhr1js3g2B4d+WrK3MN+jiDa90BRSaohdl4U7U3//v2ZOHEiZ599NsOHD68IsiAWgN1+++0AdOrUqaLn105PPvlkpeXs7GwmT55cqW3dunU0btyYBg0aHOaeSFIwDRkypNJk8Tu1a9eOV199FYgNEd/TD318/PHHvPzyy1xwwQUVXzx8W2lpKbVr1yYlJYUGDRpQq1YtgIrlOnXqcMYZZzBmzBgA5s6dy+WXXw7EeuXm5eVV5a5KkmqIjYu/pOSLVWT1P5OV3+nhlHXhmZR8sYqNn38FQDSy+/yNR51wTKXl+pnHsHrG/nsDRyORSstbvlq7268/1s9sxpav1u5x+4Oxbt5SopEIJ//0B3z98QKikQhfTf2UE684m3rNmvD1xwsO+bGDzJ5dkgLhqquu4uyzz662x8/IyOD3v/99tT2+JAVV586d99vLqmvXrpSWltKzZ086d+5M8+bNK/XKff311wmHw5x11lmccMIJzJkzp9Jyu3bt+N73vkdZWRnhcJgtW7bQvXt3IPYjJh07dqz2/ZQkJUbBfz5P9o960+H6C0ivX5f0BvU4+fofkP3D3hQMf26f921x+slknnMaodQU2lzckyYnt2LpPz886BqWjPuA5t1zaHV+d0IpKRzfuwuZ/3Eai8fGvmTfumYDRx1/9CH/CuTKD+Zx8nXn89XUWC+ur96fy8nXnc/XnyykvHTHIT1m0NmzS5IkSYdlX71vU1JS6NSpE4sWLdrnvIdPPPFEpeVv98odOHAgAwcOrLT+u8sAjzzySKXl+fPnc+qppxIKhQ5gLyRJyWjZWx8zcdADdL71UrrcHusZvHbO50wc9EBFOLQ3S/4+lbYDe3HWE79g81drmXTt79jy5dqDrqFk6UreveZ/+N5vriTv9z9n8/LVvHfTo6z790T4S9+YRpuLenDZp88SCoX4S6chBzxJPcBXU+bS+gdn8NXUWO+1r6Z+Sq2GRzlf1z4YdkmSJKla7Wl4Yzzk5OSQk5OTkOeWJMXPV1Pn7jPYGtvtxt3apg59Yg9b7t/KafP4c/aPd2tfMWkWKybN2uN9tq/fxJuX/NcBPX4oJdb769thWOGLEyh8cULF8tZV3/D8sZceRNVHHocxSpIkSZIk1QBNOrQisqNsjxPq68DZs0uSJEmSJOnfLsz/PfVPOHq39mUTPuG9Gx857Mc/+8930bz7Sbu179i0jZT0VD55YAzbN2457Oc5khl2SZIkSZIk/du48C+r9fEnXnl/tT6+HMYoSZIkSZKkADHskiRJkiRJUmAYdkmSJEmSJCkwDLskSZIkSZIUGIZdkiRJkiRJCgzDLkmSJEmSJAWGYZckSZIkSZICw7BLkiRJkiRJgWHYJUmSJEmSpMBIirBr+/btDB8+nMzMTOrWrUufPn0oKCggFAoxbty4RJcnqQos37zr9uptiatDklRZWWTX7elroDyauFokSUem1Dq1uPiDx7iyaHSiS1GSqPFhVzQaZeDAgTz99NPcfffd/OMf/yArK4sBAwYAkJubm+AKJR2Or7bAzdNgwDu72s6fAHdMh/WliatLkgSvLoXz3961fMMH8IO34R9fJKwkSdIRKPf2H7Fp+ZpEl6EkkpboAvZn1KhRjB8/nlmzZtGhQwcAwuEwWVlZZGRk0LJlywRXKOlQfb0Vrp4C60rh2x0FIsC7X0LRRniuJzRIT1SFknTkemERPDZ/9/bV22DELNhcDj/KintZkqQjTNNObTi+dxc+vudFej/zq0SXoyRR43t2jRw5kkGDBlUEXQCpqalkZWXt1qvrnnvuIRQK8emnn8a7TEmH4E8LY0FXZA/rIsDSTfDy5/GuSpK0Zhs8sWDf2zwyDzZuj089kqQjUyg1hTN/dwMf3vk0ke1liS5HSaRGh12LFi1iyZIlXHLJJbutW7ZsWaWwa8aMGXz44Ye0atUqniVKOkRbyuCfy/YcdH3b2KUQdX4YSYqrN5ZBZD/vvTsiMH55fOqRJB2ZOt54IWvnLmHVh3voaiztQ40exrhixQoAmjVrVql9wYIFFBcXV4RdpaWl/PznP+fll18mHA4f0nOFw2FSUmp09rdHHf8am8m7du06Ca4k2Hydq17t49pz4v/N2e9260qhXpOjiWwtiUNVRwaP5/jwdY4PX+fqccLP/0TjHpcTStv7OPJo2Q5+8/CzDH76ljhWFnwe0/Hh6xwfvs7xEYTXuW1aE37dsEeltgatW9D+qnN4/fu3J6iq5NW7d5jFZd8kuowqUatWLUpKDv5asEaHXU2bNgWgqKiIbt26AbEJ64cNG0YkEqkIu/7zP/+TQYMG0bp160SVKukgRbZvPaDtotEI0R3+PKMkxVNk+zYIhfa9UShEpHRLfAqSJB1xmnc7ibpHN+Li9x8FICUtlfSj6nLZvGeZNOQhe3tpn2p02JWTk0N2djZ33nkn6enp1K9fn6eeeoqZM2dSr1492rdvz7Rp05g+fTr//d//fVjPlZ+fT15eXhVVHj+nvR77t7TUn62rTr7OVS8ahR/lw5KSypPTf1sK0L1ZCp9s3hTHyoLP4zk+fJ3jw9e5ery/Cn7x0b63CaWm8drIX/K9p38Zn6KOEB7T8eHrHB++zvERhNd5VcEC/nXh3ZXalrzxAV9O2TUS5JhT29Pj/37O62f/im1rN8a7xKQyaVI+zbudlOgyEqpGj9tLS0tj7NixHHvssQwePJihQ4fSr18/evXqRadOnUhJSWHy5MnMnz+frKwsWrduzfLly/mP//gPJkyYkOjyJe1DKARXZe896ILYfF5Xto1XRZKknc5oBq3r7/1EMQVo3whyM+JZlSTpSFK+dTtbvlpX8Ve6diNEo2z5ap2T1Wu/anTYBdC5c2emTZvGli1bKCws5Gc/+xkzZ86sGMJ4xx138OWXX7J06VKWLl3KCSecwFtvvcU555yT4Mol7c/5J8DV7WK3v/1mtPP2rzrC6c2+ey9JUnVLCcH/dYdmdWPLOwc07vz3+KPgf7vtf6SjJElVZeW0efw5+8eJLkNJokYPY9yTrVu3UlhYyNChQxNdiqTDFArBz3Mgrxm8shTmrIu1dT8GLm0d6zUgSUqM44+Cv4ThH8tiv567thSOqQMXZEK/TDgq6c4iJUnSkSLpTlPmzJlDeXl5Rc+u71q6dGl8C5J02Lo0jf1JkmqW+ulwWZvYnyRJUrJIurCre/fuRKP7muVHkiRJkiRJR6oaP2eXJEmSJEmSdKAMuyRJkiRJkhQYhl2SJEmSJEkKDMMuSZIkSZIkBYZhlyRJkiRJkgLDsEuSJEmSJEmBYdglSZIkSZKkwDDskiRJkiRJUmAYdkmSJEmSJCkwDLskSYqz/Px8srKyCIfDDB48eLf1o0ePZuLEiQD07NmT+vXrs3z58t22GzFiBLm5uYTDYcaMGQPAT3/6U3r16kWPHj1YuHAhAFdffTXRaLQa90iSgqOq3qN/85vfcOaZZ9KzZ0/mzp0LwODBg+nRowd9+/ZlxYoVgO/RklQdDLskSUqAIUOGkJ+fT6NGjZg2bVqldW+88QZ9+/YF4JVXXuHSSy/d6+M89thj5Ofnc8UVVwDw5JNPMnnyZB588EH+8Ic/ANC9e3cmTZpUTXsiScFTFe/R119/PR988AHPP/88Dz74IAD33HMPU6dO5c477+SJJ54AfI+WpOpg2CVJUgJ17Nix4tt9gHXr1lGnTh1CoRAALVq02Of9hw4dyvnnn8/SpUsBSE9PB2DTpk107NgRgN69ezN+/PhqqF6Sgu1w3qNbt24NQGpqasX2e2rzPVqSqp5hlyRJCTRt2jSys7MrlouKimjVqtUB3fcXv/gF06dPZ8SIEQwbNqyivW/fvtx000107doVgFatWlUMaZQkHbjDeY/eafjw4dx4440Vy5FIhAceeIAhQ4YAvkdLUnUw7JIkKQGeeeYZwuEwxx9/PF26dDmkx2jSpAkAXbt2Ze3atRXt77zzDq+++iojRoyogkol6chTFe/RAH/605/IysoiLy+vom348OFcdtlltGnTpgoqlSTtSVqiC5Ak6Ug0ZMgQ7r777t3as7OzKS4uPqDHKCkpoUGDBnz++eccddRRAJSWllK7dm0aNWpE3bp1ASguLqZdu3ZVV7wkBVxVvEdPmzaN8ePH87e//a2i7W9/+xvr1q3j/vvvr2jzPVqSqp49uyRJqkEyMjLYsmVLxfKgQYOYMGECl19+Oe+88w4At912GwDDhg0jLy+PwYMH89vf/haAAQMG0Lt3b6655hruuusuACZNmkS/fv3ivCeSFDwH8x59xx13UFxcTJ8+fbj55psBuPXWW5k9ezbhcLgi8PI9WpKqnj27JEmKs3A4TDgc3uv6/v37M3HiRM4++2xeeuml3dY//PDDQOyXF79rT5Mcf/TRR1x33XWHXrAkHUGq6j168uTJu63bU68w36MlqeoZdkmSVMNcddVVVfp4zz33XJU+niQdyXyPlqSaz2GMkiRJkiRJCgzDLkmSJEmSJAWGYZckSZIkSZICw7BLkiRJkiRJgWHYJUmSJEmSpMAw7JIkSZIkSVJgGHZJkiRJkiQpMAy7JEmSJEmSFBiGXZIkSZIkSQoMwy5JkiRJkiQFhmGXJEmSJEmSAsOwS5IkSZIkSYFh2CVJkiRJkqTAMOySJEmSJElSYCRN2LV9+3aGDx9OZmYmdevWpU+fPhQUFBAKhRg3blyiy5MkqUJp+a7bX21JXB2SpN0t37zr9o5I4uqQtG89Hvk5Py5+mSuLRlf8Hd+7S6LLUpJIS3QBByIajTJw4EAKCgoYMWIE2dnZjBkzhgEDBgCQm5ub4AolSYKyCDyzEP6yZFdb/4nQ/Ri4tSO0aZC42iTpSLdwA/x+Hny8ZlfbuW/BFW3hJ+0gNZS42iTt2cKXJvLRXc8kugwloaQIu0aNGsX48eOZNWsWHTp0ACAcDpOVlUVGRgYtW7ZMcIWSpCNdJAp3z4CJX1ZujwIFq+HqKfBsD2jbMCHlSdIRbcEGuHYqbC+v3L5hBzy1AL7YBCNyIWTgJUmBkBTDGEeOHMmgQYMqgi6A1NRUsrKyKnp1tW7dmpNOOokuXbrQpUsX3nrrrUSVK0k6Ar23cvega6cIsLUcHpob15IkSf/24OxY0LW3UYv/XA4Fa/ayUlLCtLm4J5d/9hwXvfcInX5xMaHUpIgwVAPU+J5dixYtYsmSJTz66KO7rVu2bBmXXHJJxfLYsWPp2LFjPMuTJAmAsUshJRTr4bUnkShMXwvFm6BV/biWJklHtIUbYN76fW+TEoKxS2LDziXVDJ89M57pvx3NtnUlNO3Uhl5PDiW1di1m/s9fEl2akkCND7tWrFgBQLNmzSq1L1iwgOLi4iqbryscDpOSknwpcce/bgOgdu06Ca4k2HydFSQez9XjpD99QXrj5vvd7rR+A9lY8HocKjoyeDwraDymq17jnpeTecvz+9wmEoU3Z33Ooz1z4lPUEcLjOT6C8Dq3TWvCrxv2qNS2bu6uSVDXzl7MzN/9P3J/9SPDrgPQu3eYxWXfJLqMKlGrVi1KSkoO+n41Pt1p2rQpAEVFRRVt0WiUYcOGEYlEKoVdV155JZ06deLGG29k/fr18S5VknQEi+4oPbDtynZUcyWSpG+LHPD784FtJylBIlFwXj0doBrfsysnJ4fs7GzuvPNO0tPTqV+/Pk899RQzZ86kXr16tG/fHoApU6aQmZlJaWkpQ4cO5aabbuKll1464OfJz88nLy+vunaj2pz2784BpaV+OFcnX2cFicdz9Rg5B/62dO/zwQDUToGl7/2d+unxqir4PJ4VNB7TVW99KZw7Acr2MswcYtfP14VzuMXXvUp5PMdHEF7nVQUL+NeFd1dqa93/TFZMmsWOki00yWlF59sGsvQf0xJUYXKZNCmf5t1OSnQZCVXjw660tDTGjh3LDTfcwODBg8nMzGTo0KE0bNiQxYsXVww9zMzMBKB27drceOON9O/fP5FlS5KOMAOz4G/FxH5+cQ9CwIBWGHRJUpw1rg3nZ8LrX+z5LToEpIbg4lbxrkzSvpz0k//gjJHXkZKeytZV61k8djJzHnst0WUpSdT4sAugc+fOTJtWOcF9/PHH6dWrFwCbN2+mrKyMRo0aEY1G+ctf/kKXLl0SUKkk6UjVpgHcmwv/OROI7urhlULsdtej4eaTE1efJB3JftUx9gMhs9btel+G2O2UEDx4GpxwVAILlLSbNy/+r0SXoCSWFGHXd23dupXCwkKGDh0KwKpVq7jkkksoLy+nvLyck08+mSeffDKxRUqSjjjnngBtG8Bfl8Kkr2B7JBaCXdoazj0e0mr8TJmSFEx10+DJM+BfK2JDzpdugjop0Oc4+GFW7L1akhQcSRl2zZkzh/Ly8orJ6du0acPMmTMTXJUkSdCuEdzVOfYnSao5aqXChS1jf5KkYEvKsKt79+5Eo/uYYVKSJEmSJElHJAdUSJIkSZIkKTAMuyRJkiRJkhQYhl2SJEmSJEkKDMMuSZIkSZIkBYZhlyRJkiRJkgLDsEuSJEmSJEmBYdglSZIkSZKkwDDskiRJkiRJUmAYdkmSJEmSJCkwDLukw5Sfn09WVhbhcJjBgwfvtn706NFMnDgRgAceeICePXty880377bd6tWrGTBgAL1792bUqFEAPPvss5x55plcdNFFlJSUsGPHDvLy8ujVqxf9+/dn27ZtrFu3jltvvbV6d1JHjIM5nnv27En9+vVZvnz5btuNGDGC3NxcwuEwY8aMAeCnP/0pvXr1okePHixcuBCAq6++mmg0Wo17JEmSdGSrquuV2bNnk5eXR+/evVm2bBkA4XCYcDhMhw4dGDp0KAC33347eXl59O3bl7Vr11JYWEheXh49e/bkl7/8JQDz589n5MiR1bTHkmGXVCWGDBlCfn4+jRo1Ytq0aZXWvfHGG/Tt25dVq1YxY8YMpkyZQlpaGtOnT6+03b333ssjjzzCpEmTuO666ygrK+OFF15g6tSpXHvttTz99NOkp6czefJkJk+eTNeuXZkwYQIZGRmsX7+eTZs2xXOXFWAHcjwDvPLKK1x66aV7fZzHHnuM/Px8rrjiCgCefPJJJk+ezIMPPsgf/vAHALp3786kSZOqaU8kSZIEVXe98tprr/HII49UBFX5+fnk5+fTv39/zjvvPDZs2MDs2bN5//33GTJkCC+//DJHH300b731FlOmTGHNmjUsXLiQnJwcZsyY4ZeeqjaGXVIV6tixIytWrKhYXrduHXXq1CEUCjF9+nR69eoFQJ8+fSgoKKh034ULF3LHHXdwzjnnUFhYyJo1a2jZsiUpKSmccsopFR9KaWlpAEQiEdq2bQvEAoP8/Pw47KGOJPs6ngFatGixz/sPHTqU888/n6VLlwKQnp4OwKZNm+jYsSMAvXv3Zvz48dVQvSRJkr7rcK5XNm/eTLNmzejcuTNFRUWV1uXn5xMOh6lXrx6NGjUiEomwYcMGMjIyaNq0KfXr1wcgNTW14lyyTZs2zJs3rzp3V0cwwy6pCk2bNo3s7OyK5aKiIlq1agXA+vXradiwIQANGjRg/fr1le47depU7rvvPp544gnuuusujjnmGIqKiti2bRvvvfceGzZsAOCzzz6ja9euvP3222RmZgLQqlWrimFhUlXZ1/G8P7/4xS+YPn06I0aMYNiwYRXtffv25aabbqJr166Ax64kSVI8Hc71SiQS2ePtnY9Rq1Yt0tPTOeGEEzjppJN47LHHGDBgQMV28+bNY82aNbRr1w7wPFDVy7BLqgLPPPMM4XCY448/ni5duuxxm8aNG7Nx40YASkpKaNy4caX1HTp0oG3btrRr144NGzaQmprK7bffzrnnnsvs2bNp3rw5ACeffDIff/wxAwcO5IUXXqjO3dIR6kCO5/1p0qQJAF27dmXt2rUV7e+88w6vvvoqI0aMqIJKJUmSdCCq4nolJSVlj7fHjRvHhRdeCMTm4lqxYgWFhYXcf//9/P73vwdg48aN3HTTTRVzE0vVzbBLqgI7x8Dfd999ldqzs7MpLi4G4NRTT+W9994DYNKkSXTr1q3Stq1bt2bNmjWsWrWKevXqAXDxxReTn59P9+7d6devHzt27KgY196oUSPq1KkDQHFxccU3JNLhOpDjeX9KSkoA+PzzzznqqKMAKC0tBWLHbt26dQGPXUmSpHioiuuVevXqsXr1aubMmVOpd9j48eM577zzgFiPryZNmhAKhSrmFo5Go1xzzTXce++9HHfccRX38zxQ1Skt0QVIQZaRkcGWLVuA2PxGnTp1omfPnnTq1InTTjuNlStXMnr0aG6//XbuvvtuLr74YsrKynj00UcBuPHGGyksLOTkk0/mkUceYcmSJQwZMoSUlBSaNGnCSy+9BMCHH35YcR+punz7eAYYNGgQ7777LosXL2bEiBH07duX2267jYcffphhw4Yxe/ZsUlJSeOKJJwAYMGAAW7duJRQKVbRNmjSJfv36JWR/JEmSjnQHc70yfPhwLrroItLT03nxxRcBWLNmDenp6TRq1AiIjVaJRqOcddZZRKNRXnjhBfLz83n33XdZs2YNAA8//DCnnnoqRUVFFfO4SlUtFD3Cf/7g/fffp0ePHkydOpW8vLxEl3PQTns99u/0/omtI+gO53V+8cUXOe644zj77LOrtqh/W7duHb/97W8rughL+1OTjuerr76aZ555plJXeOlg+DmooPGYVpB4PMfH4b7O1X29sifz589n3Lhx3HHHHQCsKljAvy68O27PH3T9xt1H824nJbqMhLJnl1TNrrrqqmp9/IyMDIMuxU1VH8/PPfdclT6eJEmSDk51X6/sSU5ODjk5OXF/Xh05/CpdkiRJkiRJgWHYJUmSJEmSpMAw7JIkSZIkSVJgGHZJkiRJkiQpMAy7JEmSJEmSFBiGXZIkSZIkSQoMwy5JkiRJkiQFhmGXJEmSJEmSAsOwS5IkSZIkSYFh2CVJkiRJkqTAMOySJEmSJElSYBh2SZIkSZIkKTAMuyRJkiRJkhQYhl2SJEmSJEkKjKQJu7Zv387w4cPJzMykbt269OnTh4KCAkKhEOPGjUt0eQqwDdt33S6LJK4OqSpsL991e3NZ4uqQJEnS4dv2rXO7bZ7bSRXSEl3AgYhGowwcOJCCggJGjBhBdnY2Y8aMYcCAAQDk5uYmuEIF0cqt8OR8mLBiV9sFb8PlbWFQW0gJJa426WBtL4dnFsErS3a1nfMm/CATbsyBRrUSV5skSZIOztYy+GMhvFa8q+2ct+DCVvCzk6BeUlzpH5gT+n6P3GGX0bDtcZRt2sqnf3iDeU+9nuiyVMMlxf8Co0aNYvz48cyaNYsOHToAEA6HycrKIiMjg5YtWya4QgXNyi0weAp8Uwrf7sy1uhQe/QyKNsI9uRAy8FISKIvALwvgo9WV20sj8GoxTF8Lz/Yw8JIkSUoG28rgZx/AvPUQ/Vb7lnL4y+cway38MS8YgddxvTpzxkPXM/WWx1k5bR5pdWtz1PFHJ7osJYGkGMY4cuRIBg0aVBF0AaSmppKVlVXRq2vbtm387Gc/o127dpxyyilcd911iSpXAfC7T3cPur5t/HKYvDKuJUmH7LXi3YOunaLAF5vgDwviWpIkSZIO0Uufw6frKwddO0WBBRvgxaI4F1VNcn99GXMe+RtfTZ1LtDzCjk1bWV+4LNFlKQnU+LBr0aJFLFmyhEsuuWS3dcuWLasIu379619Tp04dFi5cyNy5c/ntb38b71IVEF9vjQVZ+5qeKyUEryyNV0XS4fnrEthXJ8Qo8MayWHd4SZIk1VzlURi7ZN/bRIG/LU3++YbT6tbm6C5tqXtMYwZM+T9+NOdp+jw/jPqZzRJdmpJAje/YuGJFbMKkZs0qH9ALFiyguLiY3NxcNm3axIsvvsjy5csJ/XtcWfPmzQ/qecLhMCkpNT77203Hv24DoHbtOgmuJDjqd/kPsu7a9xjwSBTe/3wdtcPHxqkq6RClpnHKXzbvd7Nt5dAi51S2ffFpHIqSqo6fgwoaj2kFicdz1Utr1Iycp/ffs+mb7dDo+DaUrVux321rgrZpTfh1wx6V2mo1PopQSgqtzu/O25ffx9a1G+h279X0fuZ23jjn9gRVmhx69w6zuOybRJdRJWrVqkVJSclB36/GpztNmzYFoKhoVz/MaDTKsGHDiEQi5ObmsnjxYpo2bco999zDaaedRjgcZurUqYkqWckucoDdWyLl+99GSrRohGj0wL7Wi3pMS5Ik1WjR8oPoip/k53Y7NsXC0s+eHs+m5asp37qdGQ+OoekpWc7bpf2q8T27cnJyyM7O5s477yQ9PZ369evz1FNPMXPmTOrVq0f79u2ZNWsWn3/+Obm5uTz00EN89NFHXHDBBRQVFdGwYcMDep78/Hzy8vKqeW+q3mn/7oBUWlqa2EICZNMO+I+3YpN3700K0L/DMczydVcSuP59mLl230Nzj64NHy6aRVqN/wpEqszPQQWNx7SCxOO56kWjcHk+LC7Z85xdEJu+olV9+HhlcdL8oNaqggX868K7K7XtKNnCpmVfx3ZaB2XSpHyadzsp0WUkVI2/rElLS2Ps2LEce+yxDB48mKFDh9KvXz969epFp06dSElJoWXLlqSlpXH55ZcD0L17d44++mgWLlyY4OqVjOqnx36yd1+fCxHgh1nxqkg6PJe32XfQBfCjNhh0SZIk1XChEFzRdu9BF8TWXd4mGL8cX/jiBHKuPZ96xzUltXY6ub++jDWzF7N5xZpEl6Yarsb37ALo3Lkz06ZNq9T2+OOP06tXLwCOPvpoevfuzdtvv80555zDwoUL+frrr8nOzk5EuQqAW3Jg4QaYtS4Weu38MEkJxebr+lVH6NgkkRVKBy58LPwkG54vin3DsTP42nk73AJ+3DZx9UmSJOnAXZAJc7+J/eL2ns7tLsiEAa0SV19VmvvEOGo1qk//Cf8DoRS+LljApCEPJbosJYGkCLu+a+vWrRQWFjJ06NCKtj/84Q9cc8013HbbbaSnpzN69GgaN26csBqV3OqkwRNnwLgvYr9kV7wJUkPQozlc1gZOc4i4ksxNJ0OnDPh/S2D6mlhoe2KjWA/F8zNjx7ckSZJqvlAI7uwE3Y6OndvNWRdr79gk1lv/nOOC0asLgGiUT+5/iU/ufynRlSjJJGXYNWfOHMrLy8nNza1oa9OmDfn5+YkrSoFTOzUWBPwwKxYMhAjQh4aOSGe1iP1Fo7Heiikez5IkSUkpFILvHx/72zmlldcq0i5JGXZ1796dqJPUKY4MBRQkodC+56STJElS8jDkknbndMSSJEmSJEkKDMMuSZIkSZIkBYZhlyRJkiRJkgLDsEuSJEmSJEmBYdglSZIkSZKkwDDskiRJkiRJUmAYdkmSJEmSJCkwDLskSZIkSZIUGIZdkiRJkiRJCgzDrgDLz88nKyuLcDjM4MGDd1s/evRoJk6cCMADDzxAz549ufnmm3fb7ic/+QlnnHEG4XCYd999F4DevXsTDocJh8N89tln7Nixg7y8PHr16kX//v3Ztm0b69at49Zbb63enZQkSTpCHMy5Xc+ePalfvz7Lly/fbbvf/OY3nHnmmfTs2ZO5c+cCMHjwYHr06EHfvn1ZsWIFAFdffTXRaLQa90hHuqq6Xpk9ezZ5eXn07t2bZcuWAVRcq3To0IGhQ4cCcPvtt5OXl0ffvn1Zu3YthYWF5OXl0bNnT375y18CMH/+fEaOHFlNeywpXgy7Am7IkCHk5+fTqFEjpk2bVmndG2+8Qd++fVm1ahUzZsxgypQppKWlMX369N0e55VXXiE/P58+ffoAkJqaSn5+Pvn5+Zx88smkp6czefJkJk+eTNeuXZkwYQIZGRmsX7+eTZs2xWVfJUmSgu5Azu0gdu526aWX7vExrr/+ej744AOef/55HnzwQQDuuecepk6dyp133skTTzwBQPfu3Zk0aVI17o1UNdcr9957L6+99hqPPPJIRVC181qlf//+nHfeeWzYsIHZs2fz/vvvM2TIEF5++WWOPvpo3nrrLaZMmcKaNWtYuHAhOTk5zJgxw6BXSnKGXUeIjh07VnxLB7Bu3Trq1KlDKBRi+vTp9OrVC4A+ffpQUFBQ6b6hUIgf/ehH/PCHP2TdunUARKNRwuEw1157Ldu2bQMgLS0NgEgkQtu2bYHYSVJ+fn51754kSXu0v14D999/P4sWLdrnY9x888307NmTBx54YLd1BQUF5OXlceaZZ3L33XfvtrzTH//4R/r27Us4HGbHjh32HNBh29e5HUCLFi32et/WrVsDsS8vd26/p7bevXszfvz4aqhe2t3hXK9s3ryZZs2a0blzZ4qKiiqty8/PJxwOU69ePRo1akQkEmHDhg1kZGTQtGlT6tevD1Q+9tu0acO8efOqc3clVTPDriPEtGnTyM7OrlguKiqiVatWAKxfv56GDRsC0KBBA9avX1/pvv/7v//L+++/z49+9KOKb//Gjh1Lfn4+OTk5PPPMMwB89tlndO3albfffpvMzEwAWrVqxcKFC6t79yRJ2qu99RqIRCLMmTOHdu3a7fW+06dPJy0tjSlTpjBjxgxWrVpVaX1ubi7vv/8+H3zwAdOmTaN9+/aVljdu3MgXX3zB3Llzeeedd8jPzyc9Pd2eAzps+zq3O1DDhw/nxhtvrFiORCI88MADDBkyBPA8TvF1ONcrkUhkj7d3PkatWrVIT0/nhBNO4KSTTuKxxx5jwIABFdvNmzePNWvWVHweeOxLyc+wK+CeeeYZwuEwxx9/PF26dNnjNo0bN2bjxo0AlJSU0Lhx40rrmzRpAkD//v0rvuHYU9vJJ5/Mxx9/zMCBA3nhhReqYW8kSTp03+01MHv2bE488cR93uejjz6qGMLfq1cvPvnkk0rr09PTASgvL+e4447jqKOOqrRcr149JkyYwObNm+nTpw8jRoyouK89B3QoDuTc7kD86U9/Iisri7y8vIq24cOHc9lll9GmTZsqqFQ6MFVxvZKSkrLH2+PGjePCCy8EYnNxrVixgsLCQu6//35+//vfA7Bx40ZuuukmRo0aVYV7JSnRDLsCbue32ffdd1+l9uzsbIqLiwE49dRTee+99wCYNGkS3bp1q7RtSUkJEPu2JSsra49tO3bsqPh2ulGjRtSpUweA4uLifX5jLklSvHy318CiRYsqhm7tzf56EwCMGTOGnJwcGjduTFpa2m7LX3/9NaFQiHfffZclS5Ywa9YswJ4DOjQHcm63P9OmTWP8+PGVwte//e1vrFu3jmuuuaaizfM4xUNVXK/Uq1eP1atXM2fOnErv8+PHj+e8884DYj2+mjRpQigUqphbOBqNcs0113Dvvfdy3HHHVdzPY19KfmmJLkCJkZGRwZYtW4DYnA6dOnWiZ8+edOrUidNOO42VK1cyevRobr/9dq666ipWr15N7dq1ef755wE4++yzqVWrFo0bN+all16iuLiYIUOGkJKSQpMmTXjppZcA+PDDD3n00UcTtZuSJPHMM88wceJEevTosddeA5999lml4VwA55577m69CXbOSfltV1xxBZdddhkDBw5k7ty5uy03atSoYq6Zs846i8LCwsPqkSPtybfP7QAGDRrEu+++y+LFixkxYgR9+/bltttu4+GHH+aOO+6gpKSEPn36cMopp/DYY49x6623cvzxxxMOh/n+97/PXXfdxaRJk+jXr18C90pHsoO5Xhk+fDgXXXQR6enpvPjiiwCsWbOG9PR0GjVqBECHDh2IRqOcddZZRKNRXnjhBfLz83n33XdZs2YNAA8//DCnnnoqRUVFdOzYMTE7LqlKGHYF2M6f292b/v37M3HiRM4++2yGDx/O8OHDK9a1aNGC22+/HYDXXnttt/t+9NFHlZYbNWrE5MmTK7WtW7eOxo0b06BBg8PYC0mSDs+QIUMqTRa/U7t27Xj11VeB2FD8Pf2gyscff8zLL7/MBRdcwHvvvccPf/jDSutLS0upXbs2KSkpNGjQgFq1agFULNepU4czzjiDMWPGADB37lwuv/xyINZz4NtDyKT9OZhzu51fPH7bww8/DLDbORuwx15hH330Edddd92hFyztR1Vdr+ycP/Hbjj76aN58881Kbd8dqtimTZuKH+Daaf78+Zx66qkVk9VLSk4OYzyCXXXVVZx99tnV9vgZGRkVY+ElSappOnfuTGFh4T636dq1K6WlpfTs2ZPOnTvTvHlzVq5cyUMPPQTA66+/Tjgc5qyzzuKEE05gzpw5lZbbtWvH9773PcrKygiHw2zZsoXu3bsD2HNAVa6qz+2ee+65SvMfSfFW3dcre5KTk8Mdd9wR1+eUVPXs2SVJkgJrX70GUlJS6NSpE4sWLdrn3CxPPPFEpeVv9yYYOHAgAwcOrLT+u8sAjzzySKVlew5IkiRVH8MuSZJ0xNrT8MZ4yMnJIScnJyHPLUmSFHT2S5YkSZIkSVJgGHZJkiRJkiQpMAy7JEmSJEmSFBiGXZIkSZIkSQoMwy5JkiRJkiQFhmGXJEmSJEmSAsOwS5IkSZIkSYFh2CVJkiRJkqTAMOySJEmSJElSYBh2SZIkSZIkKTAMuyRJkiRJkhQYhl2SJEmSJEkKjKQJu7Zv387w4cPJzMykbt269OnTh4KCAkKhEOPGjUt0eQmxuSzRFUiSVDNs2pHoCqTD53GsINlevut2JJq4OiQdmdISXcCBiEajDBw4kIKCAkaMGEF2djZjxoxhwIABAOTm5ia4wvhavhmeWQhvrtjV9pP34CftIHxs4uqSJClepqyE5xbtWj77Tfj+8fDTE6Fl/cTVJR2Kz0vg6UJ456tdbddNhWtOhNObJa4u6VBs2hG7VnmteFfbgHfgirYwsDWkhBJWmpLMlUWjKy2n1kpn/aIVvN73tgRVpGSSFGHXqFGjGD9+PLNmzaJDhw4AhMNhsrKyyMjIoGXLlgmuMH6WlMCQqbEPkci32j9bD7/6GH7ZAa5sm6jqJEmqfq8sgZFzK3dPL4vCWytiIdjTPSC7YcLKkw7KZ+vh+vehtLzyud2sdXDzh/CfXeCCI+dUV0muZEfsWmVJCXy7M9eXW+ChufDZN/BfuQZeOjB/zv5xpeX+7zzMknHvJ6gaJZukGMY4cuRIBg0aVBF0AaSmppKVlUVubi5Lly6lS5cuFX+tW7cmIyMjgRVXn/+aCZvKKp8Mwa7l38+DpSXxrkqSpPhYvhn+Z27s9m6fhVHYUgZ3fQJRh8woCUSicOf03YMuiC1Hgd/OhtXbElCcdAiemL970AW7lv+5HN7+Mt5VKQiO7pJN4xNPoOj/TUp0KUoSNT7sWrRoEUuWLOGSSy7Zbd2yZcvIzc2ldevWzJo1q+Lvoosu4oorrkhAtdVr/vrYt3/7GvMeAv5WvPf1kiQls1f38xkXARaXwJxv4lKOdFgKVsPyLbsHXd8WicLfPbdTEti0A974Yveg69tSgL8uiVdFCpJ2V/Rhxbsz2brKD3gdmBo/jHHFitjEVM2aVZ6wYMGCBRQXF+82X9f27dv585//zFtvvXVQzxMOh0lJqdnZX8Y513P8Tx/d5zZR4Jm3Crjz1J7xKUqSpDjKGvE29Tuctd/t+l17O2v/ue/PTCnRjrn4Dppf9l+EQns/B41GIjz08lvc9OBF8StMOgR123Uj+4Ep+9wmAsz6eju1azeIT1FKGm3TmvDrhj32uC6tbm2yLsxjyi2Px7mq5NW7d5jFZcEIBmvVqkVJycEPX6vZ6Q7QtGlTAIqKiiraotEow4YNIxKJ7BZ2vf766xx//PF873vfi2udcRHd1/d+397OsRuSpICKRogeyOecn4VKBp7bKUgO8Hj2cNbBan3BGZRt3c7yiZ8kuhQlkVD0gM4YE6esrIycnBx27NjBQw89RP369XnqqaeYOXMm69ato6SkpFKPrPPOO49zzz2XW2655YAef+PGjcydO5dTTjmFhg1r9my2izfCj/L3vU0IGJwNN50cj4okSYqvPy6Apxfue5gMwEtnwUmN41GRdOhmrYVrD2Cu5VtOhquyq78e6XBsK4P/mACby/a+TUoIuh4NT5wRv7qUHFYVLOBfF969x3X9xv2Wrz9awCcP/DnOVSWvfuPuo3m3kxJdRkLV+J5daWlpjB07lmOPPZbBgwczdOhQ+vXrR69evejUqVOloGvFihVMnjyZK6+88oAfv2HDhuTl5dX4oAugbUP4XtN9/0cLAQNaxasiSZLia0Crff+KVwrQsbFBl5JD5wxo22Dvx3QIqJUC/TPjWpZ0SOqkxd6j9/VDi5Eo/DArbiUpABq2PY5mp7Vn4cvvJLoUJZkaH3YBdO7cmWnTprFlyxYKCwv52c9+xsyZM3cbwvjCCy9w/vnnVwx9DKJ7c+HoOrt/iKQQa/vPLnD8UfGvS5KkeGhWF+7JjX3ufTcgCAFNasN9pyaiMunghUIw8jRomL6Hc7tQ7O+BU6Fx7YSUJx20G9rDKU12b9950XllGzireVxLUpJrd3kfVn00n5IlKxNdipJMjZ+gfk+2bt1KYWEhQ4cOrdT+/PPP8+ijwZ6MtkU9GH0WvLQYXiuGTWWxk6O85vDj7FjPL0mSguzcE+DYejC6CCavjA1pPCoNLmwZ+yw8pk6iK5QOXOsGsWG3oxfD61/A1vJYyNWnBQzKho57CA6kmqpOGjx5Jvy/z+GVpbBya6z95CZwRRv4/nGxkFc6UJ/c91KiS1CSqvFzdu3JRx99xOmnn87HH3/MaaedluhyEqY8Cpt3QO3U2J8kSUea7eWwrRyOSodUL6CU5MoisfmO6qZCLc/tlOSi0djxnBqCuknZxULxtK85u3TwnLMrSXt2de/e/cB+iSngUkPQsFaiq5AkKXFqGQooQNJSoJHndgqIUAjqpye6CklHqqSYs0uSJEmSJEk6EIZdkiRJkiRJCgzDLkmSJEmSJAWGYZckSZIkSZICw7BLkiRJkiRJgWHYJUmSJEmSpMAw7JIkSZIkSVJgGHZJkiRJkiQpMAy7JEmSJEmSFBihaDQaTXQRkiRJkiTpyLS9ZAvfzP8i0WUERpOcltRqUC/RZSSUYZckSZIkSZICw2GMkiRJkiRJCgzDLkmSJEmSJAWGYZckSZIkSZICw7BLkiRJkiRJgWHYJUmSJEmSpMAw7JIkSZIkSVJgGHZJkiRJkiQpMAy7JEmSJEmSFBiGXZIkSZIkSQoMwy5JkiRJkiQFhmGXJEmSJEmSAsOwS5IkSZIkSYFh2CVJkiRJkqTAMOySJEmSJElSYBh2SZIkSZIkKTAMuyRJkiRJkhQYhl2SJEmSJEkKDMMuSZIkSZIkBYZhlyRJkiRJkgLj/wOejdBn3rAAGQAAAABJRU5ErkJggg==", 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    " ] @@ -776,10 +776,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:15.791942Z", - "iopub.status.busy": "2024-10-30T01:18:15.791589Z", - "iopub.status.idle": "2024-10-30T01:18:16.048206Z", - "shell.execute_reply": "2024-10-30T01:18:16.047702Z" + "iopub.execute_input": "2024-10-30T02:10:01.512769Z", + "iopub.status.busy": "2024-10-30T02:10:01.512568Z", + "iopub.status.idle": "2024-10-30T02:10:01.773917Z", + "shell.execute_reply": "2024-10-30T02:10:01.773307Z" } }, "outputs": [ diff --git a/dev/explanations/state-vectors-and-gates.ipynb b/dev/explanations/state-vectors-and-gates.ipynb index 4485797a4..4c54e759f 100644 --- a/dev/explanations/state-vectors-and-gates.ipynb +++ b/dev/explanations/state-vectors-and-gates.ipynb @@ -26,10 +26,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:18.353825Z", - "iopub.status.busy": "2024-10-30T01:18:18.353580Z", - "iopub.status.idle": "2024-10-30T01:18:19.070555Z", - "shell.execute_reply": "2024-10-30T01:18:19.070020Z" + "iopub.execute_input": "2024-10-30T02:10:04.418025Z", + "iopub.status.busy": "2024-10-30T02:10:04.417480Z", + "iopub.status.idle": "2024-10-30T02:10:05.125267Z", + "shell.execute_reply": "2024-10-30T02:10:05.124634Z" } }, "outputs": [ @@ -74,10 +74,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:19.072948Z", - "iopub.status.busy": "2024-10-30T01:18:19.072497Z", - "iopub.status.idle": "2024-10-30T01:18:19.079196Z", - "shell.execute_reply": "2024-10-30T01:18:19.078623Z" + "iopub.execute_input": "2024-10-30T02:10:05.127739Z", + "iopub.status.busy": "2024-10-30T02:10:05.127148Z", + "iopub.status.idle": "2024-10-30T02:10:05.134277Z", + "shell.execute_reply": "2024-10-30T02:10:05.133694Z" } }, "outputs": [ @@ -120,10 +120,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:19.081471Z", - "iopub.status.busy": "2024-10-30T01:18:19.080918Z", - "iopub.status.idle": "2024-10-30T01:18:19.085462Z", - "shell.execute_reply": "2024-10-30T01:18:19.084996Z" + "iopub.execute_input": "2024-10-30T02:10:05.136245Z", + "iopub.status.busy": "2024-10-30T02:10:05.135916Z", + "iopub.status.idle": "2024-10-30T02:10:05.140337Z", + "shell.execute_reply": "2024-10-30T02:10:05.139769Z" } }, "outputs": [ @@ -157,10 +157,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:19.087595Z", - "iopub.status.busy": "2024-10-30T01:18:19.087118Z", - "iopub.status.idle": "2024-10-30T01:18:19.091217Z", - "shell.execute_reply": "2024-10-30T01:18:19.090667Z" + "iopub.execute_input": "2024-10-30T02:10:05.142414Z", + "iopub.status.busy": "2024-10-30T02:10:05.141917Z", + "iopub.status.idle": "2024-10-30T02:10:05.146148Z", + "shell.execute_reply": "2024-10-30T02:10:05.145687Z" } }, "outputs": [ @@ -199,10 +199,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:19.093127Z", - "iopub.status.busy": "2024-10-30T01:18:19.092764Z", - "iopub.status.idle": "2024-10-30T01:18:19.098476Z", - "shell.execute_reply": "2024-10-30T01:18:19.097893Z" + "iopub.execute_input": "2024-10-30T02:10:05.148356Z", + "iopub.status.busy": "2024-10-30T02:10:05.147900Z", + "iopub.status.idle": "2024-10-30T02:10:05.153848Z", + "shell.execute_reply": "2024-10-30T02:10:05.153312Z" } }, "outputs": [ @@ -245,10 +245,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:19.100480Z", - "iopub.status.busy": "2024-10-30T01:18:19.100097Z", - "iopub.status.idle": "2024-10-30T01:18:19.105467Z", - "shell.execute_reply": "2024-10-30T01:18:19.104906Z" + "iopub.execute_input": "2024-10-30T02:10:05.155963Z", + "iopub.status.busy": "2024-10-30T02:10:05.155611Z", + "iopub.status.idle": "2024-10-30T02:10:05.161396Z", + "shell.execute_reply": "2024-10-30T02:10:05.160798Z" } }, "outputs": [ @@ -293,10 +293,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:19.107433Z", - "iopub.status.busy": "2024-10-30T01:18:19.107085Z", - "iopub.status.idle": "2024-10-30T01:18:19.112091Z", - "shell.execute_reply": "2024-10-30T01:18:19.111524Z" + "iopub.execute_input": "2024-10-30T02:10:05.163370Z", + "iopub.status.busy": "2024-10-30T02:10:05.163050Z", + "iopub.status.idle": "2024-10-30T02:10:05.168129Z", + "shell.execute_reply": "2024-10-30T02:10:05.167557Z" } }, "outputs": [ diff --git a/dev/genindex.html b/dev/genindex.html index 4f67d1111..56c9cae03 100644 --- a/dev/genindex.html +++ b/dev/genindex.html @@ -279,10 +279,6 @@

    _

  97. (ffsim.qiskit.DropNegligible method)
  98. (ffsim.qiskit.FfsimSampler method) -
  99. -
  100. (ffsim.qiskit.GivensAnsatzOperatorJW method) -
  101. -
  102. (ffsim.qiskit.GivensAnsatzOperatorSpinlessJW method)
  103. (ffsim.qiskit.GivensAnsatzOpJW method)
  104. @@ -309,32 +305,12 @@

    _

  105. (ffsim.qiskit.SimulateTrotterDiagCoulombSplitOpJW method)
  106. (ffsim.qiskit.SimulateTrotterDoubleFactorizedJW method) -
  107. -
  108. (ffsim.qiskit.UCJOperatorJW method)
  109. (ffsim.qiskit.UCJOpSpinBalancedJW method)
  110. (ffsim.qiskit.UCJOpSpinlessJW method)
  111. (ffsim.qiskit.UCJOpSpinUnbalancedJW method) -
  112. - - - @@ -489,22 +465,10 @@

    D

  113. (ffsim.UCJOpSpinless attribute)
  114. (ffsim.UCJOpSpinUnbalanced attribute) -
  115. - -
  116. diag_coulomb_mats_alpha_alpha (ffsim.RealUCJOperator attribute) - -
  117. <~IL=#22>-zw7 zb>NTKc^UyV@h4$_!=)qn*ubff2hMfHZTn2>%YeR`xY{+T^GA9cLUEtbZ2>8@O1Oh* zwB2(iC3yG4ce>sNNZyW)Ork0v6RfNCCy+BqU~p$}eS8}`O1G6qB_`e?cLqX@kH0p{ z!=2$g-r`H=jLau2kx(?RqV|C{8e>;&NU#;sMFDbw{y|Pn@d{Dv;Lf4S?5^3V?Pe8< zb*T+j_aZK?<{lxH$~v#za61v7i6@G_F99!+!wg3)UYW4cVMZ{&J!Xp+-m8kjZJt>N zb`;lDAQ?0hql)*z*+g-BMn*;VBsUmR^Q)AMYMsKHPu3IAEYUNy?*m=5?ooMP|uWQF!_XM)AZ&#BNKvg(vdp z(WCmaDS|?N&OgbI-C@7}mjd&@*XmB-M1DNJ5&&D=Rn^t8u&V|lxJFDY07Nr<#8v}C zeM>PvCkA|VtPP`u8S}cvFRT((apdZs$`WM9JYTc_zu+2k!tzBOi^e9O+RuGwGaT3H zH!R3s|26>&_ml0990p>%+){nB8EIxq=K4>=D8Su8wWziF#lSaq=+oT*lW=7_iI14C z%6SuYD#=P?B&)b2ZZvs`2M2bN0`-cT8?2*+^T5nMd9UF^vSyuD;vJo`>`ezbg>MTg zeUbR*<9yPqlcM@#)+2kPPUnnj?8xhV)vRpLO!Xh69t@14jQuc%-+G{ zB_~A@U-|ZhY}Yf_mkX?&Sr=X}F>GkJxvVGeIdw5N@&5VPIQmqW)MOR=y-1uhjxTr0 zEf{VIN@$6xV>FZ&Ct8rw-bJ)&3DKvSTrn2GX6z?<+EC=UrR+|dS1{ayKRsM%a2gY) z5|*36vxF=4`t|FbNA(@trMXf#L$bJWwLCJ|j#}SzfVI!tclUb-;wCC-C0Wb&87nVs z9A2vQP&|_U5c{Q!Goj#9U6h$O-Fj-=PKVbo$Ul$cI`DwR{?VMzxv$GX81!nsyz1U| 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index 604b4cddd..f548d47c1 100644 --- a/dev/.doctrees/nbsphinx/how-to-guides/entanglement-forging.ipynb +++ b/dev/.doctrees/nbsphinx/how-to-guides/entanglement-forging.ipynb @@ -18,10 +18,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:20.784495Z", - "iopub.status.busy": "2024-10-30T01:18:20.783947Z", - "iopub.status.idle": "2024-10-30T01:18:21.769365Z", - "shell.execute_reply": "2024-10-30T01:18:21.768728Z" + "iopub.execute_input": "2024-10-30T02:10:07.008010Z", + "iopub.status.busy": "2024-10-30T02:10:07.007818Z", + "iopub.status.idle": "2024-10-30T02:10:08.016452Z", + "shell.execute_reply": "2024-10-30T02:10:08.015851Z" } }, "outputs": [ @@ -36,8 +36,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Parsing /tmp/tmpq74vr6zi\n", - "converged SCF energy = -75.6787887956313\n" + "Parsing /tmp/tmpj9k_slbv\n", + "converged SCF energy = -75.6787887956314\n" ] }, { @@ -125,10 +125,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:21.773036Z", - "iopub.status.busy": "2024-10-30T01:18:21.772098Z", - "iopub.status.idle": "2024-10-30T01:18:21.777732Z", - "shell.execute_reply": "2024-10-30T01:18:21.777297Z" + "iopub.execute_input": "2024-10-30T02:10:08.019464Z", + "iopub.status.busy": "2024-10-30T02:10:08.018977Z", + "iopub.status.idle": "2024-10-30T02:10:08.023867Z", + "shell.execute_reply": "2024-10-30T02:10:08.023409Z" } }, "outputs": [], @@ -168,10 +168,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:21.779956Z", - "iopub.status.busy": "2024-10-30T01:18:21.779603Z", - "iopub.status.idle": "2024-10-30T01:18:21.783008Z", - "shell.execute_reply": "2024-10-30T01:18:21.782541Z" + "iopub.execute_input": "2024-10-30T02:10:08.025897Z", + "iopub.status.busy": "2024-10-30T02:10:08.025525Z", + "iopub.status.idle": "2024-10-30T02:10:08.028728Z", + "shell.execute_reply": "2024-10-30T02:10:08.028239Z" } }, "outputs": [], @@ -200,10 +200,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:21.785197Z", - "iopub.status.busy": "2024-10-30T01:18:21.784707Z", - "iopub.status.idle": "2024-10-30T01:18:21.903053Z", - "shell.execute_reply": "2024-10-30T01:18:21.902536Z" + "iopub.execute_input": "2024-10-30T02:10:08.030425Z", + "iopub.status.busy": "2024-10-30T02:10:08.030233Z", + "iopub.status.idle": "2024-10-30T02:10:08.160649Z", + "shell.execute_reply": "2024-10-30T02:10:08.160112Z" } }, "outputs": [ @@ -211,7 +211,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Energy at initialialization: -75.6779440365972\n" + "Energy at initialialization: -75.67794403659721\n" ] } ], @@ -238,10 +238,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:21.905261Z", - "iopub.status.busy": "2024-10-30T01:18:21.904895Z", - "iopub.status.idle": "2024-10-30T01:18:29.911065Z", - "shell.execute_reply": "2024-10-30T01:18:29.910404Z" + "iopub.execute_input": "2024-10-30T02:10:08.162889Z", + "iopub.status.busy": "2024-10-30T02:10:08.162485Z", + "iopub.status.idle": "2024-10-30T02:10:16.097588Z", + "shell.execute_reply": "2024-10-30T02:10:16.096992Z" } }, "outputs": [ @@ -253,10 +253,10 @@ " message: STOP: TOTAL NO. of f AND g EVALUATIONS EXCEEDS LIMIT\n", " success: False\n", " status: 1\n", - " fun: -75.68381561441599\n", - " x: [-1.603e-01 6.417e-03 ... 5.748e-02 -1.005e-01]\n", + " fun: -75.68381560395008\n", + " x: [-1.603e-01 6.420e-03 ... 5.748e-02 -1.005e-01]\n", " nit: 3\n", - " jac: [ 2.146e-04 1.066e-04 ... -4.742e-03 7.439e-03]\n", + " jac: [ 2.117e-04 1.080e-04 ... -4.744e-03 7.439e-03]\n", " nfev: 112\n", " njev: 7\n", " hess_inv: <15x15 LbfgsInvHessProduct with dtype=float64>\n" diff --git a/dev/.doctrees/nbsphinx/how-to-guides/fermion-operator.ipynb b/dev/.doctrees/nbsphinx/how-to-guides/fermion-operator.ipynb index 4c2fa2c5d..2a4c92a88 100644 --- a/dev/.doctrees/nbsphinx/how-to-guides/fermion-operator.ipynb +++ b/dev/.doctrees/nbsphinx/how-to-guides/fermion-operator.ipynb @@ -29,10 +29,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:31.626849Z", - "iopub.status.busy": "2024-10-30T01:18:31.626414Z", - "iopub.status.idle": "2024-10-30T01:18:32.335681Z", - "shell.execute_reply": "2024-10-30T01:18:32.335062Z" + "iopub.execute_input": "2024-10-30T02:10:17.655526Z", + "iopub.status.busy": "2024-10-30T02:10:17.655306Z", + "iopub.status.idle": "2024-10-30T02:10:18.376628Z", + "shell.execute_reply": "2024-10-30T02:10:18.376105Z" } }, "outputs": [ @@ -40,8 +40,8 @@ "data": { "text/plain": [ "FermionOperator({\n", - " (cre_a(3), des_a(0)): -0.25,\n", " (cre_a(0), des_a(3)): 0.5,\n", + " (cre_a(3), des_a(0)): -0.25,\n", " (cre_b(1), des_b(5), cre_a(4)): 1+1j\n", "})" ] @@ -76,17 +76,17 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:32.338076Z", - "iopub.status.busy": "2024-10-30T01:18:32.337631Z", - "iopub.status.idle": "2024-10-30T01:18:32.341635Z", - "shell.execute_reply": "2024-10-30T01:18:32.341152Z" + "iopub.execute_input": "2024-10-30T02:10:18.378984Z", + "iopub.status.busy": "2024-10-30T02:10:18.378440Z", + "iopub.status.idle": "2024-10-30T02:10:18.382615Z", + "shell.execute_reply": "2024-10-30T02:10:18.382029Z" } }, "outputs": [ { "data": { "text/plain": [ - "'FermionOperator({((True, False, 3), (False, False, 0)): -0.25+0j, ((True, False, 0), (False, False, 3)): 0.5+0j, ((True, True, 1), (False, True, 5), (True, False, 4)): 1+1j})'" + "'FermionOperator({((True, False, 0), (False, False, 3)): 0.5+0j, ((True, False, 3), (False, False, 0)): -0.25+0j, ((True, True, 1), (False, True, 5), (True, False, 4)): 1+1j})'" ] }, "execution_count": 2, @@ -110,10 +110,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:32.343438Z", - "iopub.status.busy": "2024-10-30T01:18:32.343105Z", - "iopub.status.idle": "2024-10-30T01:18:32.347916Z", - "shell.execute_reply": "2024-10-30T01:18:32.347401Z" + "iopub.execute_input": "2024-10-30T02:10:18.384486Z", + "iopub.status.busy": "2024-10-30T02:10:18.384294Z", + "iopub.status.idle": "2024-10-30T02:10:18.388544Z", + "shell.execute_reply": "2024-10-30T02:10:18.388093Z" } }, "outputs": [ @@ -121,17 +121,17 @@ "data": { "text/plain": [ "FermionOperator({\n", - " (cre_a(3), des_a(0)): -0.5,\n", - " (cre_b(2)): 0-0.25j,\n", " (des_a(3), des_b(3)): 0.0625,\n", " (cre_a(0), des_a(3), des_a(3), des_b(3)): -0.125,\n", + " (cre_b(1), des_b(5), cre_a(4)): 2+2j,\n", + " (cre_b(2)): 0-0.25j,\n", + " (cre_a(0), des_a(3), cre_b(2)): 0+0.5j,\n", + " (cre_a(0), des_a(3)): 1,\n", " (cre_a(3), des_a(0), cre_b(2)): 0-0.25j,\n", - " (cre_a(3), des_a(0), des_a(3), des_b(3)): 0.0625,\n", " (cre_b(1), des_b(5), cre_a(4), des_a(3), des_b(3)): -0.25-0.25j,\n", + " (cre_a(3), des_a(0)): -0.5,\n", " (cre_b(1), des_b(5), cre_a(4), cre_b(2)): -1+1j,\n", - " (cre_b(1), des_b(5), cre_a(4)): 2+2j,\n", - " (cre_a(0), des_a(3)): 1,\n", - " (cre_a(0), des_a(3), cre_b(2)): 0+0.5j\n", + " (cre_a(3), des_a(0), des_a(3), des_b(3)): 0.0625\n", "})" ] }, @@ -170,10 +170,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:32.350103Z", - "iopub.status.busy": "2024-10-30T01:18:32.349653Z", - "iopub.status.idle": "2024-10-30T01:18:32.354064Z", - "shell.execute_reply": "2024-10-30T01:18:32.353537Z" + "iopub.execute_input": "2024-10-30T02:10:18.390321Z", + "iopub.status.busy": "2024-10-30T02:10:18.390134Z", + "iopub.status.idle": "2024-10-30T02:10:18.394167Z", + "shell.execute_reply": "2024-10-30T02:10:18.393568Z" } }, "outputs": [ @@ -181,17 +181,17 @@ "data": { "text/plain": [ "FermionOperator({\n", - " (cre_a(3), des_a(0)): 0+3j,\n", - " (cre_b(2)): -5,\n", " (des_a(3), des_b(3)): 0-1.25j,\n", " (cre_a(0), des_a(3), des_a(3), des_b(3)): 0+0.5j,\n", + " (cre_b(1), des_b(5), cre_a(4)): 12-12j,\n", + " (cre_b(2)): -5,\n", + " (cre_a(0), des_a(3), cre_b(2)): 2,\n", + " (cre_a(0), des_a(3)): 0-6j,\n", " (cre_a(3), des_a(0), cre_b(2)): -1,\n", - " (cre_a(3), des_a(0), des_a(3), des_b(3)): 0-0.25j,\n", " (cre_b(1), des_b(5), cre_a(4), des_a(3), des_b(3)): -1+1j,\n", + " (cre_a(3), des_a(0)): 0+3j,\n", " (cre_b(1), des_b(5), cre_a(4), cre_b(2)): 4+4j,\n", - " (cre_b(1), des_b(5), cre_a(4)): 12-12j,\n", - " (cre_a(0), des_a(3)): 0-6j,\n", - " (cre_a(0), des_a(3), cre_b(2)): 2\n", + " (cre_a(3), des_a(0), des_a(3), des_b(3)): 0-0.25j\n", "})" ] }, @@ -220,10 +220,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:32.355985Z", - "iopub.status.busy": "2024-10-30T01:18:32.355631Z", - "iopub.status.idle": "2024-10-30T01:18:32.359216Z", - "shell.execute_reply": "2024-10-30T01:18:32.358769Z" + "iopub.execute_input": "2024-10-30T02:10:18.396225Z", + "iopub.status.busy": "2024-10-30T02:10:18.395751Z", + "iopub.status.idle": "2024-10-30T02:10:18.399643Z", + "shell.execute_reply": "2024-10-30T02:10:18.399057Z" } }, "outputs": [ @@ -232,15 +232,15 @@ "text/plain": [ "FermionOperator({\n", " (cre_b(1), cre_a(4), des_b(5)): -12+12j,\n", - " (cre_a(3), des_a(0)): 0+3j,\n", - " (cre_b(2), cre_b(1), cre_a(4), des_b(5)): 4+4j,\n", " (cre_a(3), des_b(3), des_a(3), des_a(0)): 0+0.25j,\n", - " (cre_a(0), des_a(3)): 0-6j,\n", - " (cre_b(2), cre_a(0), des_a(3)): 2,\n", + " (cre_a(3), des_a(0)): 0+3j,\n", " (cre_b(2)): -5,\n", + " (cre_b(1), cre_a(4), des_b(5), des_b(3), des_a(3)): -1+1j,\n", " (cre_b(2), cre_a(3), des_a(0)): -1,\n", + " (cre_b(2), cre_b(1), cre_a(4), des_b(5)): 4+4j,\n", + " (cre_a(0), des_a(3)): 0-6j,\n", " (des_b(3), des_a(3)): 0+1.25j,\n", - " (cre_b(1), cre_a(4), des_b(5), des_b(3), des_a(3)): -1+1j\n", + " (cre_b(2), cre_a(0), des_a(3)): 2\n", "})" ] }, @@ -265,10 +265,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:32.361089Z", - "iopub.status.busy": "2024-10-30T01:18:32.360740Z", - "iopub.status.idle": "2024-10-30T01:18:32.364058Z", - "shell.execute_reply": "2024-10-30T01:18:32.363588Z" + "iopub.execute_input": "2024-10-30T02:10:18.401500Z", + "iopub.status.busy": "2024-10-30T02:10:18.401181Z", + "iopub.status.idle": "2024-10-30T02:10:18.404577Z", + "shell.execute_reply": "2024-10-30T02:10:18.404088Z" } }, "outputs": [ @@ -298,10 +298,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:32.365943Z", - "iopub.status.busy": "2024-10-30T01:18:32.365605Z", - "iopub.status.idle": "2024-10-30T01:18:32.369440Z", - "shell.execute_reply": "2024-10-30T01:18:32.368998Z" + "iopub.execute_input": "2024-10-30T02:10:18.406311Z", + "iopub.status.busy": "2024-10-30T02:10:18.406121Z", + "iopub.status.idle": "2024-10-30T02:10:18.410376Z", + "shell.execute_reply": "2024-10-30T02:10:18.409893Z" } }, "outputs": [ @@ -341,21 +341,21 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:32.371236Z", - "iopub.status.busy": "2024-10-30T01:18:32.371051Z", - "iopub.status.idle": "2024-10-30T01:18:32.377092Z", - "shell.execute_reply": "2024-10-30T01:18:32.376575Z" + "iopub.execute_input": "2024-10-30T02:10:18.412330Z", + "iopub.status.busy": "2024-10-30T02:10:18.411996Z", + "iopub.status.idle": "2024-10-30T02:10:18.417875Z", + "shell.execute_reply": "2024-10-30T02:10:18.417419Z" } }, "outputs": [ { "data": { "text/plain": [ - "array([0. +0.j , 0. +0.j ,\n", - " 0. +0.j , 0. +0.j ,\n", - " 0.04133437-0.09265645j, 0. +0.j ,\n", - " 0. +0.j , 0. +0.j ,\n", - " 0. +0.j ])" + "array([ 0. +0.j , 0. +0.j ,\n", + " 0. +0.j , 0. +0.j ,\n", + " -0.00869096+0.0819987j, 0. +0.j ,\n", + " 0. +0.j , 0. +0.j ,\n", + " 0. +0.j ])" ] }, "execution_count": 8, @@ -380,10 +380,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:32.378997Z", - "iopub.status.busy": "2024-10-30T01:18:32.378664Z", - "iopub.status.idle": "2024-10-30T01:18:32.389860Z", - "shell.execute_reply": "2024-10-30T01:18:32.389289Z" + "iopub.execute_input": "2024-10-30T02:10:18.419713Z", + "iopub.status.busy": "2024-10-30T02:10:18.419509Z", + "iopub.status.idle": "2024-10-30T02:10:18.430769Z", + "shell.execute_reply": "2024-10-30T02:10:18.430174Z" } }, "outputs": [ diff --git a/dev/.doctrees/nbsphinx/how-to-guides/lucj.ipynb b/dev/.doctrees/nbsphinx/how-to-guides/lucj.ipynb index 85e2c0dc9..12fc5224d 100644 --- a/dev/.doctrees/nbsphinx/how-to-guides/lucj.ipynb +++ b/dev/.doctrees/nbsphinx/how-to-guides/lucj.ipynb @@ -16,10 +16,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:34.180013Z", - "iopub.status.busy": "2024-10-30T01:18:34.179829Z", - "iopub.status.idle": "2024-10-30T01:18:35.169572Z", - "shell.execute_reply": "2024-10-30T01:18:35.168967Z" + "iopub.execute_input": "2024-10-30T02:10:20.152623Z", + "iopub.status.busy": "2024-10-30T02:10:20.152421Z", + "iopub.status.idle": "2024-10-30T02:10:21.236157Z", + "shell.execute_reply": "2024-10-30T02:10:21.235561Z" } }, "outputs": [ @@ -27,15 +27,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "converged SCF energy = -77.8266321248744\n" + "converged SCF energy = -77.8266321248745\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Parsing /tmp/tmpd8_rkfdb\n", - "converged SCF energy = -77.8266321248744\n" + "Parsing /tmp/tmpfosa4q8z\n", + "converged SCF energy = -77.8266321248745\n" ] }, { @@ -123,10 +123,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:35.172742Z", - "iopub.status.busy": "2024-10-30T01:18:35.172170Z", - "iopub.status.idle": "2024-10-30T01:18:35.241838Z", - "shell.execute_reply": "2024-10-30T01:18:35.241283Z" + "iopub.execute_input": "2024-10-30T02:10:21.239362Z", + "iopub.status.busy": "2024-10-30T02:10:21.238897Z", + "iopub.status.idle": "2024-10-30T02:10:21.314071Z", + "shell.execute_reply": "2024-10-30T02:10:21.313410Z" } }, "outputs": [ @@ -134,14 +134,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "E(CCSD) = -77.8742153637403 E_corr = -0.0475832388658442\n" + "E(CCSD) = -77.87421536374035 E_corr = -0.04758323886585828\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Energy at initialization: -77.87160024816285\n" + "Energy at initialization: -77.87160024816286\n" ] }, { @@ -189,10 +189,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:18:35.244267Z", - "iopub.status.busy": "2024-10-30T01:18:35.244046Z", - "iopub.status.idle": "2024-10-30T01:20:21.406793Z", - "shell.execute_reply": "2024-10-30T01:20:21.406123Z" + "iopub.execute_input": "2024-10-30T02:10:21.316975Z", + "iopub.status.busy": "2024-10-30T02:10:21.316391Z", + "iopub.status.idle": "2024-10-30T02:12:06.948791Z", + "shell.execute_reply": "2024-10-30T02:12:06.948101Z" } }, "outputs": [ @@ -204,10 +204,10 @@ " message: STOP: TOTAL NO. of ITERATIONS REACHED LIMIT\n", " success: False\n", " status: 1\n", - " fun: -77.8738739196927\n", - " x: [-1.153e+00 -3.619e-05 ... 2.464e-04 1.287e-01]\n", + " fun: -77.87387391803198\n", + " x: [-1.153e+00 2.068e-04 ... 2.121e-04 1.286e-01]\n", " nit: 10\n", - " jac: [ 3.837e-05 3.268e-05 ... 2.132e-05 1.990e-05]\n", + " jac: [ 7.105e-06 6.395e-05 ... 7.105e-06 4.263e-06]\n", " nfev: 949\n", " njev: 13\n", " hess_inv: <72x72 LbfgsInvHessProduct with dtype=float64>\n" @@ -251,10 +251,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:20:21.410468Z", - "iopub.status.busy": "2024-10-30T01:20:21.409450Z", - "iopub.status.idle": "2024-10-30T01:20:58.217540Z", - "shell.execute_reply": "2024-10-30T01:20:58.216961Z" + "iopub.execute_input": "2024-10-30T02:12:06.951693Z", + "iopub.status.busy": "2024-10-30T02:12:06.951430Z", + "iopub.status.idle": "2024-10-30T02:12:42.952939Z", + "shell.execute_reply": "2024-10-30T02:12:42.952336Z" } }, "outputs": [ @@ -266,10 +266,10 @@ " message: CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n", " success: True\n", " status: 0\n", - " fun: -77.87363426548542\n", - " x: [-1.152e+00 -1.070e-04 ... 3.519e-02 2.561e-01]\n", + " fun: -77.87363426219025\n", + " x: [-1.152e+00 -1.229e-04 ... 3.520e-02 2.561e-01]\n", " nit: 5\n", - " jac: [-2.700e-05 -1.563e-05 ... 5.684e-06 1.421e-06]\n", + " jac: [ 4.832e-05 1.137e-05 ... 5.684e-06 0.000e+00]\n", " nfev: 329\n", " njev: 7\n", " hess_inv: <46x46 LbfgsInvHessProduct with dtype=float64>\n" @@ -314,10 +314,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:20:58.221118Z", - "iopub.status.busy": "2024-10-30T01:20:58.220286Z", - "iopub.status.idle": "2024-10-30T01:21:07.477266Z", - "shell.execute_reply": "2024-10-30T01:21:07.476669Z" + "iopub.execute_input": "2024-10-30T02:12:42.955527Z", + "iopub.status.busy": "2024-10-30T02:12:42.955059Z", + "iopub.status.idle": "2024-10-30T02:13:08.712793Z", + "shell.execute_reply": "2024-10-30T02:13:08.712175Z" } }, "outputs": [ @@ -328,29 +328,29 @@ "Number of parameters: 46\n", " message: Convergence: Relative reduction of objective function <= ftol.\n", " success: True\n", - " fun: -77.87363429187597\n", - " x: [-1.152e+00 7.038e-04 ... 3.490e-02 2.558e-01]\n", + " fun: -77.87363432090041\n", + " x: [-1.152e+00 -6.041e-05 ... 3.502e-02 2.560e-01]\n", " nit: 3\n", - " jac: [ 1.841e-06 7.770e-06 ... -5.057e-07 -3.093e-06]\n", - " nfev: 439\n", + " jac: [-1.510e-08 1.524e-07 ... -1.810e-07 -5.001e-07]\n", + " nfev: 589\n", " njev: 4\n", - " nlinop: 255\n", + " nlinop: 405\n", "\n", "Iteration 1\n", - " Energy: -77.87363142328681\n", - " Norm of gradient: 0.0012661679917073813\n", - " Regularization hyperparameter: 0.0017699397462047916\n", - " Variation hyperparameter: 0.9960170698760089\n", + " Energy: -77.87362715516973\n", + " Norm of gradient: 0.002343171963637818\n", + " Regularization hyperparameter: 0.00449294068954832\n", + " Variation hyperparameter: 0.9982898802893294\n", "Iteration 2\n", - " Energy: -77.87363428112481\n", - " Norm of gradient: 6.884314263972443e-05\n", - " Regularization hyperparameter: 0.0017699397462047916\n", - " Variation hyperparameter: 0.9960170698760089\n", + " Energy: -77.87363431035497\n", + " Norm of gradient: 6.191997144810509e-05\n", + " Regularization hyperparameter: 0.00449294287499052\n", + " Variation hyperparameter: 0.9982898801014709\n", "Iteration 3\n", - " Energy: -77.87363429187597\n", - " Norm of gradient: 2.9829016234855147e-05\n", - " Regularization hyperparameter: 0.058627037136415205\n", - " Variation hyperparameter: 0.9945772210228708\n" + " Energy: -77.87363432090041\n", + " Norm of gradient: 4.609013221280637e-06\n", + " Regularization hyperparameter: 0.00449298279453376\n", + " Variation hyperparameter: 0.9982898814086849\n" ] } ], diff --git a/dev/.doctrees/nbsphinx/how-to-guides/qiskit-circuits.ipynb b/dev/.doctrees/nbsphinx/how-to-guides/qiskit-circuits.ipynb index 87efd9b84..8f11cb6e2 100644 --- a/dev/.doctrees/nbsphinx/how-to-guides/qiskit-circuits.ipynb +++ b/dev/.doctrees/nbsphinx/how-to-guides/qiskit-circuits.ipynb @@ -16,10 +16,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:09.040585Z", - "iopub.status.busy": "2024-10-30T01:21:09.040114Z", - "iopub.status.idle": "2024-10-30T01:21:09.753345Z", - "shell.execute_reply": "2024-10-30T01:21:09.752667Z" + "iopub.execute_input": "2024-10-30T02:13:10.494310Z", + "iopub.status.busy": "2024-10-30T02:13:10.494117Z", + "iopub.status.idle": "2024-10-30T02:13:11.200660Z", + "shell.execute_reply": "2024-10-30T02:13:11.200122Z" } }, "outputs": [], @@ -54,10 +54,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:09.756048Z", - "iopub.status.busy": "2024-10-30T01:21:09.755598Z", - "iopub.status.idle": "2024-10-30T01:21:10.341887Z", - "shell.execute_reply": "2024-10-30T01:21:10.341262Z" + "iopub.execute_input": "2024-10-30T02:13:11.203123Z", + "iopub.status.busy": "2024-10-30T02:13:11.202628Z", + "iopub.status.idle": "2024-10-30T02:13:11.785044Z", + "shell.execute_reply": "2024-10-30T02:13:11.784473Z" } }, "outputs": [ @@ -103,10 +103,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:10.344282Z", - "iopub.status.busy": "2024-10-30T01:21:10.343617Z", - "iopub.status.idle": "2024-10-30T01:21:10.402946Z", - "shell.execute_reply": "2024-10-30T01:21:10.402321Z" + "iopub.execute_input": "2024-10-30T02:13:11.787136Z", + "iopub.status.busy": "2024-10-30T02:13:11.786831Z", + "iopub.status.idle": "2024-10-30T02:13:11.846025Z", + "shell.execute_reply": "2024-10-30T02:13:11.845534Z" } }, "outputs": [ @@ -160,17 +160,17 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:10.405087Z", - "iopub.status.busy": "2024-10-30T01:21:10.404733Z", - "iopub.status.idle": "2024-10-30T01:21:10.408685Z", - "shell.execute_reply": "2024-10-30T01:21:10.408200Z" + "iopub.execute_input": "2024-10-30T02:13:11.847982Z", + "iopub.status.busy": "2024-10-30T02:13:11.847787Z", + "iopub.status.idle": "2024-10-30T02:13:11.851795Z", + "shell.execute_reply": "2024-10-30T02:13:11.851325Z" } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -195,17 +195,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:10.410622Z", - "iopub.status.busy": "2024-10-30T01:21:10.410283Z", - "iopub.status.idle": "2024-10-30T01:21:10.415199Z", - "shell.execute_reply": "2024-10-30T01:21:10.414606Z" + "iopub.execute_input": "2024-10-30T02:13:11.853887Z", + "iopub.status.busy": "2024-10-30T02:13:11.853497Z", + "iopub.status.idle": "2024-10-30T02:13:11.858375Z", + "shell.execute_reply": "2024-10-30T02:13:11.857782Z" } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -242,17 +242,17 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:10.417227Z", - "iopub.status.busy": "2024-10-30T01:21:10.416817Z", - "iopub.status.idle": "2024-10-30T01:21:10.421220Z", - "shell.execute_reply": "2024-10-30T01:21:10.420749Z" + "iopub.execute_input": "2024-10-30T02:13:11.860241Z", + "iopub.status.busy": "2024-10-30T02:13:11.860046Z", + "iopub.status.idle": "2024-10-30T02:13:11.864759Z", + "shell.execute_reply": "2024-10-30T02:13:11.864182Z" } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -279,17 +279,17 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:10.422960Z", - "iopub.status.busy": "2024-10-30T01:21:10.422770Z", - "iopub.status.idle": "2024-10-30T01:21:10.426803Z", - "shell.execute_reply": "2024-10-30T01:21:10.426351Z" + "iopub.execute_input": "2024-10-30T02:13:11.866723Z", + "iopub.status.busy": "2024-10-30T02:13:11.866328Z", + "iopub.status.idle": "2024-10-30T02:13:11.870461Z", + "shell.execute_reply": "2024-10-30T02:13:11.869966Z" } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -315,17 +315,17 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:10.428895Z", - "iopub.status.busy": "2024-10-30T01:21:10.428459Z", - "iopub.status.idle": "2024-10-30T01:21:10.432821Z", - "shell.execute_reply": "2024-10-30T01:21:10.432344Z" + "iopub.execute_input": "2024-10-30T02:13:11.872189Z", + "iopub.status.busy": "2024-10-30T02:13:11.872001Z", + "iopub.status.idle": "2024-10-30T02:13:11.876447Z", + "shell.execute_reply": "2024-10-30T02:13:11.875967Z" } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -354,17 +354,17 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:10.449472Z", - "iopub.status.busy": "2024-10-30T01:21:10.449255Z", - "iopub.status.idle": "2024-10-30T01:21:10.454983Z", - "shell.execute_reply": "2024-10-30T01:21:10.454367Z" + "iopub.execute_input": "2024-10-30T02:13:11.878326Z", + "iopub.status.busy": "2024-10-30T02:13:11.878136Z", + "iopub.status.idle": "2024-10-30T02:13:11.883351Z", + "shell.execute_reply": "2024-10-30T02:13:11.882865Z" } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -391,17 +391,17 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:10.457016Z", - "iopub.status.busy": "2024-10-30T01:21:10.456649Z", - "iopub.status.idle": "2024-10-30T01:21:10.461815Z", - "shell.execute_reply": "2024-10-30T01:21:10.461246Z" + "iopub.execute_input": "2024-10-30T02:13:11.885324Z", + "iopub.status.busy": "2024-10-30T02:13:11.884972Z", + "iopub.status.idle": "2024-10-30T02:13:11.890196Z", + "shell.execute_reply": "2024-10-30T02:13:11.889600Z" } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -428,17 +428,17 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:10.463722Z", - "iopub.status.busy": "2024-10-30T01:21:10.463385Z", - "iopub.status.idle": "2024-10-30T01:21:10.469167Z", - "shell.execute_reply": "2024-10-30T01:21:10.468587Z" + "iopub.execute_input": "2024-10-30T02:13:11.892321Z", + "iopub.status.busy": "2024-10-30T02:13:11.891967Z", + "iopub.status.idle": "2024-10-30T02:13:11.897477Z", + "shell.execute_reply": "2024-10-30T02:13:11.896889Z" } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 11, diff --git a/dev/.doctrees/nbsphinx/how-to-guides/qiskit-sampler.ipynb b/dev/.doctrees/nbsphinx/how-to-guides/qiskit-sampler.ipynb index 04959ceb8..d82416e0b 100644 --- a/dev/.doctrees/nbsphinx/how-to-guides/qiskit-sampler.ipynb +++ b/dev/.doctrees/nbsphinx/how-to-guides/qiskit-sampler.ipynb @@ -18,10 +18,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:12.341251Z", - "iopub.status.busy": "2024-10-30T01:21:12.340807Z", - "iopub.status.idle": "2024-10-30T01:21:13.043042Z", - "shell.execute_reply": "2024-10-30T01:21:13.042502Z" + "iopub.execute_input": "2024-10-30T02:13:13.978104Z", + "iopub.status.busy": "2024-10-30T02:13:13.977919Z", + "iopub.status.idle": "2024-10-30T02:13:14.687070Z", + "shell.execute_reply": "2024-10-30T02:13:14.686522Z" } }, "outputs": [], @@ -71,10 +71,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:13.045546Z", - "iopub.status.busy": "2024-10-30T01:21:13.045067Z", - "iopub.status.idle": "2024-10-30T01:21:13.109043Z", - "shell.execute_reply": "2024-10-30T01:21:13.108446Z" + "iopub.execute_input": "2024-10-30T02:13:14.689609Z", + "iopub.status.busy": "2024-10-30T02:13:14.689096Z", + "iopub.status.idle": "2024-10-30T02:13:14.754005Z", + "shell.execute_reply": "2024-10-30T02:13:14.753370Z" } }, "outputs": [ @@ -154,10 +154,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:13.111071Z", - "iopub.status.busy": "2024-10-30T01:21:13.110860Z", - "iopub.status.idle": "2024-10-30T01:21:13.390112Z", - "shell.execute_reply": "2024-10-30T01:21:13.389579Z" + "iopub.execute_input": "2024-10-30T02:13:14.756485Z", + "iopub.status.busy": "2024-10-30T02:13:14.756088Z", + "iopub.status.idle": "2024-10-30T02:13:15.049078Z", + "shell.execute_reply": "2024-10-30T02:13:15.048430Z" } }, "outputs": [ @@ -174,7 +174,7 @@ "text": [ "norb = 14\n", "nelec = (3, 3)\n", - "E(CCSD) = -108.9630419334856 E_corr = -0.1278053627110057\n" + "E(CCSD) = -108.9630419334856 E_corr = -0.1278053627110062\n" ] }, { @@ -188,15 +188,15 @@ "data": { "text/plain": [ "{'0000000000011100000000000111': 9924,\n", - " '0000000000110100000000001101': 14,\n", - " '0000000001110000000000000111': 11,\n", + " '0000000000110100000000001101': 15,\n", + " '0000000001110000000000000111': 10,\n", " '0000000000011100000000011100': 10,\n", - " '0000000001011000000000010110': 9,\n", - " '0100000000100100000000000111': 5,\n", - " '0001000001010000000000000111': 4,\n", + " '0000000001011000000000010110': 10,\n", + " '0001000001010000000000000111': 5,\n", " '0000000001011000100000000110': 4,\n", + " '0100000001001000000000000111': 3,\n", " '0000000000011100100000001100': 3,\n", - " '0010000000011000000000010110': 3}" + " '0110000000001000000000000111': 3}" ] }, "execution_count": 3, @@ -276,10 +276,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:13.392385Z", - "iopub.status.busy": "2024-10-30T01:21:13.391958Z", - "iopub.status.idle": "2024-10-30T01:21:13.955792Z", - "shell.execute_reply": "2024-10-30T01:21:13.955141Z" + "iopub.execute_input": "2024-10-30T02:13:15.051726Z", + "iopub.status.busy": "2024-10-30T02:13:15.051139Z", + "iopub.status.idle": "2024-10-30T02:13:15.601185Z", + "shell.execute_reply": "2024-10-30T02:13:15.600632Z" } }, "outputs": [ @@ -294,7 +294,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "SCF energy = -75.3484557071169\n" + "SCF energy = -75.3484557067898\n" ] }, { @@ -312,7 +312,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "E(UCCSD) = -75.45619739124993 E_corr = -0.1077416841329854\n" + "E(UCCSD) = -75.45619739131405 E_corr = -0.1077416845241938" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" ] }, { diff --git a/dev/.doctrees/nbsphinx/tutorials/double-factorized-trotter.ipynb b/dev/.doctrees/nbsphinx/tutorials/double-factorized-trotter.ipynb index b9a6b100e..44c11fc1d 100644 --- a/dev/.doctrees/nbsphinx/tutorials/double-factorized-trotter.ipynb +++ b/dev/.doctrees/nbsphinx/tutorials/double-factorized-trotter.ipynb @@ -18,10 +18,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:15.569628Z", - "iopub.status.busy": "2024-10-30T01:21:15.569178Z", - "iopub.status.idle": "2024-10-30T01:21:16.343351Z", - "shell.execute_reply": "2024-10-30T01:21:16.342737Z" + "iopub.execute_input": "2024-10-30T02:13:17.168226Z", + "iopub.status.busy": "2024-10-30T02:13:17.168027Z", + "iopub.status.idle": "2024-10-30T02:13:17.936164Z", + "shell.execute_reply": "2024-10-30T02:13:17.935579Z" } }, "outputs": [ @@ -80,10 +80,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:16.346874Z", - "iopub.status.busy": "2024-10-30T01:21:16.345993Z", - "iopub.status.idle": "2024-10-30T01:21:16.350701Z", - "shell.execute_reply": "2024-10-30T01:21:16.350135Z" + "iopub.execute_input": "2024-10-30T02:13:17.939927Z", + "iopub.status.busy": "2024-10-30T02:13:17.938796Z", + "iopub.status.idle": "2024-10-30T02:13:17.943572Z", + "shell.execute_reply": "2024-10-30T02:13:17.943105Z" } }, "outputs": [], @@ -106,10 +106,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:16.353106Z", - "iopub.status.busy": "2024-10-30T01:21:16.352571Z", - "iopub.status.idle": "2024-10-30T01:21:16.357024Z", - "shell.execute_reply": "2024-10-30T01:21:16.356607Z" + "iopub.execute_input": "2024-10-30T02:13:17.945653Z", + "iopub.status.busy": "2024-10-30T02:13:17.945468Z", + "iopub.status.idle": "2024-10-30T02:13:17.949666Z", + "shell.execute_reply": "2024-10-30T02:13:17.949250Z" } }, "outputs": [ @@ -172,10 +172,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:16.358880Z", - "iopub.status.busy": "2024-10-30T01:21:16.358417Z", - "iopub.status.idle": "2024-10-30T01:21:16.362588Z", - "shell.execute_reply": "2024-10-30T01:21:16.362093Z" + "iopub.execute_input": "2024-10-30T02:13:17.951616Z", + "iopub.status.busy": "2024-10-30T02:13:17.951157Z", + "iopub.status.idle": "2024-10-30T02:13:17.954889Z", + "shell.execute_reply": "2024-10-30T02:13:17.954334Z" } }, "outputs": [ @@ -208,10 +208,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:16.364481Z", - "iopub.status.busy": "2024-10-30T01:21:16.364033Z", - "iopub.status.idle": "2024-10-30T01:21:16.367796Z", - "shell.execute_reply": "2024-10-30T01:21:16.367311Z" + "iopub.execute_input": "2024-10-30T02:13:17.956639Z", + "iopub.status.busy": "2024-10-30T02:13:17.956465Z", + "iopub.status.idle": "2024-10-30T02:13:17.960198Z", + "shell.execute_reply": "2024-10-30T02:13:17.959619Z" } }, "outputs": [ @@ -242,10 +242,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:16.369632Z", - "iopub.status.busy": "2024-10-30T01:21:16.369293Z", - "iopub.status.idle": "2024-10-30T01:21:16.388149Z", - "shell.execute_reply": "2024-10-30T01:21:16.387655Z" + "iopub.execute_input": "2024-10-30T02:13:17.962303Z", + "iopub.status.busy": "2024-10-30T02:13:17.961942Z", + "iopub.status.idle": "2024-10-30T02:13:17.980806Z", + "shell.execute_reply": "2024-10-30T02:13:17.980335Z" } }, "outputs": [ @@ -253,7 +253,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Maximum error in a tensor entry: 0.036685417309833546\n" + "Maximum error in a tensor entry: 0.03668541730983543\n" ] } ], @@ -302,10 +302,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:16.390032Z", - "iopub.status.busy": "2024-10-30T01:21:16.389842Z", - "iopub.status.idle": "2024-10-30T01:21:16.394226Z", - "shell.execute_reply": "2024-10-30T01:21:16.393720Z" + "iopub.execute_input": "2024-10-30T02:13:17.982757Z", + "iopub.status.busy": "2024-10-30T02:13:17.982412Z", + "iopub.status.idle": "2024-10-30T02:13:17.986489Z", + "shell.execute_reply": "2024-10-30T02:13:17.985972Z" } }, "outputs": [], @@ -360,10 +360,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:16.395951Z", - "iopub.status.busy": "2024-10-30T01:21:16.395760Z", - "iopub.status.idle": "2024-10-30T01:21:16.399123Z", - "shell.execute_reply": "2024-10-30T01:21:16.398652Z" + "iopub.execute_input": "2024-10-30T02:13:17.988288Z", + "iopub.status.busy": "2024-10-30T02:13:17.988101Z", + "iopub.status.idle": "2024-10-30T02:13:17.991662Z", + "shell.execute_reply": "2024-10-30T02:13:17.991096Z" } }, "outputs": [], @@ -400,10 +400,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:16.400966Z", - "iopub.status.busy": "2024-10-30T01:21:16.400625Z", - "iopub.status.idle": "2024-10-30T01:21:16.499955Z", - "shell.execute_reply": "2024-10-30T01:21:16.499401Z" + "iopub.execute_input": "2024-10-30T02:13:17.993552Z", + "iopub.status.busy": "2024-10-30T02:13:17.993366Z", + "iopub.status.idle": "2024-10-30T02:13:18.093585Z", + "shell.execute_reply": "2024-10-30T02:13:18.092940Z" } }, "outputs": [], @@ -439,10 +439,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:16.503025Z", - "iopub.status.busy": "2024-10-30T01:21:16.502100Z", - "iopub.status.idle": "2024-10-30T01:21:16.552205Z", - "shell.execute_reply": "2024-10-30T01:21:16.551691Z" + "iopub.execute_input": "2024-10-30T02:13:18.096418Z", + "iopub.status.busy": "2024-10-30T02:13:18.095964Z", + "iopub.status.idle": "2024-10-30T02:13:18.151819Z", + "shell.execute_reply": "2024-10-30T02:13:18.151155Z" } }, "outputs": [ @@ -450,7 +450,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Fidelity of Trotter-evolved state with exact state: 0.9402435115158829\n" + "Fidelity of Trotter-evolved state with exact state: 0.9402393562171665\n" ] } ], @@ -480,10 +480,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:16.554365Z", - "iopub.status.busy": "2024-10-30T01:21:16.553983Z", - "iopub.status.idle": "2024-10-30T01:21:16.766608Z", - "shell.execute_reply": "2024-10-30T01:21:16.766088Z" + "iopub.execute_input": "2024-10-30T02:13:18.154078Z", + "iopub.status.busy": "2024-10-30T02:13:18.153717Z", + "iopub.status.idle": "2024-10-30T02:13:18.377572Z", + "shell.execute_reply": "2024-10-30T02:13:18.376941Z" } }, "outputs": [ @@ -491,7 +491,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Fidelity of Trotter-evolved state with exact state: 0.998521285420148\n" + "Fidelity of Trotter-evolved state with exact state: 0.998521121422965\n" ] } ], @@ -521,10 +521,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:16.768693Z", - "iopub.status.busy": "2024-10-30T01:21:16.768296Z", - "iopub.status.idle": "2024-10-30T01:21:16.895863Z", - "shell.execute_reply": "2024-10-30T01:21:16.895300Z" + "iopub.execute_input": "2024-10-30T02:13:18.379938Z", + "iopub.status.busy": "2024-10-30T02:13:18.379561Z", + "iopub.status.idle": "2024-10-30T02:13:18.520811Z", + "shell.execute_reply": "2024-10-30T02:13:18.520167Z" } }, "outputs": [ @@ -532,7 +532,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Fidelity of Trotter-evolved state with exact state: 0.9985212854200725\n" + "Fidelity of Trotter-evolved state with exact state: 0.9985211214229502\n" ] } ], @@ -563,10 +563,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-10-30T01:21:16.897906Z", - "iopub.status.busy": "2024-10-30T01:21:16.897561Z", - "iopub.status.idle": "2024-10-30T01:21:17.005310Z", - "shell.execute_reply": "2024-10-30T01:21:17.004809Z" + "iopub.execute_input": "2024-10-30T02:13:18.522977Z", + "iopub.status.busy": "2024-10-30T02:13:18.522596Z", + "iopub.status.idle": "2024-10-30T02:13:18.625433Z", + "shell.execute_reply": "2024-10-30T02:13:18.624893Z" } }, "outputs": [ @@ -574,7 +574,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Fidelity of Trotter-evolved state with exact state: 0.9996731164188576\n" + "Fidelity of Trotter-evolved state with exact state: 0.9996731166933288\n" ] } ], diff 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