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include notebook
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@mpharrigan
mpharrigan committed Aug 29, 2024
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{
"cells": [
{
"cell_type": "markdown",
"id": "1c2b1422-5a3b-401c-88fb-0c0b723c6633",
"metadata": {},
"source": [
"# Timing Cost Computation\n",
"\n",
"This notebook goes through each bloq example and calls `report_on_cost_timings`, which currently times how long it takes to do the `QubitCount` cost key. This uses the `ExecuteWithTimeout` fixture."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9ae691c8-a5f8-4b66-a99c-7d82ad8ffc2e",
"metadata": {},
"outputs": [],
"source": [
"from qualtran_dev_tools.execute_with_timeout import ExecuteWithTimeout\n",
"from qualtran_dev_tools.bloq_report_card import report_on_cost_timings\n",
"from qualtran_dev_tools.bloq_finder import get_bloq_examples"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3fcfb561-6cb9-4891-8157-547dbfa5502b",
"metadata": {},
"outputs": [],
"source": [
"bes = get_bloq_examples()\n",
"\n",
"# Imports to exclude certain bloqs, see following comment\n",
"from qualtran.bloqs.multiplexers.apply_gate_to_lth_target import ApplyGateToLthQubit"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cea586d9-55b3-4ee8-b255-ea88bdff75f4",
"metadata": {},
"outputs": [],
"source": [
"exec = ExecuteWithTimeout(timeout=20., max_workers=4)\n",
"for i, be in enumerate(bes):\n",
"\n",
" if be.bloq_cls == ApplyGateToLthQubit:\n",
" # This bloq uses a lambda function as one of its attributes, which\n",
" # can't be pickled and used with multiprocessing.\n",
" continue\n",
" \n",
" exec.submit(report_on_cost_timings, kwargs=dict(name=be.name, cls_name=be.bloq_cls.__name__, bloq=be.make()))\n",
"\n",
"records = []\n",
"while exec.work_to_be_done:\n",
" kwargs, record = exec.next_result()\n",
" print('\\r', f'{exec.work_to_be_done:5d} remaining', end='', flush=True)\n",
" \n",
" if record is None:\n",
" records.append({\n",
" 'name': kwargs['name'],\n",
" 'cls': kwargs['cls_name'],\n",
" 'err': 'Timeout',\n",
" })\n",
" else:\n",
" records.append(record)\n",
"\n",
"import pandas as pd\n",
"df = pd.DataFrame(records)"
]
},
{
"cell_type": "markdown",
"id": "abf0d3f1-bc5f-48a6-9d9b-b9fa1bbeb8d1",
"metadata": {},
"source": [
"## Slowest\n",
"\n",
"This prints the total number of bloq examples considered and then summarizes the 5 slowest-to-compute bloq examples."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "86f259b0-ec70-4860-b76c-0df993ca7934",
"metadata": {},
"outputs": [],
"source": [
"print(len(df))\n",
"df.sort_values(by='qubitcount_dur', ascending=False).head()"
]
},
{
"cell_type": "markdown",
"id": "a30190dd-550d-45d4-a572-94fdaa4a2f10",
"metadata": {},
"source": [
"## Errors and timeouts\n",
"\n",
"These bloq examples either time-out or encounter errors in the qubit computation."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "513a3421-8f80-43ae-9449-89c8063da242",
"metadata": {},
"outputs": [],
"source": [
"df[df['qubitcount_dur'].isna()]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "605e8869-e77e-40b3-8455-07204b7b872c",
"metadata": {},
"outputs": [],
"source": [
"for i, row in df[df['qubitcount_dur'].isna()].iterrows():\n",
" print(\"### `{}`\".format(row['name']))\n",
" print(\"{}\\n\".format(row[\"err\"]))"
]
},
{
"cell_type": "markdown",
"id": "bceb65d1-d107-4c0d-8662-8ea80c00de16",
"metadata": {},
"source": [
"## Timeouts\n",
"\n",
"These examples specifically time out. "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "043c3d7a-f4d4-486c-aa99-eb871e23df3b",
"metadata": {},
"outputs": [],
"source": [
"df[df['err'] == 'Timeout']"
]
},
{
"cell_type": "markdown",
"id": "c7b87a8d-c4eb-4b7b-abce-66dfa31a0699",
"metadata": {},
"source": [
"## Investigation\n",
"\n",
"Individual bloq examples can be investigated. Strangely, hubbard_time_evolution_by_gqsp times out when run through the fixture but appears reasonably quick when run directly."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d0f486e8-8cbf-4907-a84d-9ff43cfcd43e",
"metadata": {},
"outputs": [],
"source": [
"def get_bloq_example(name):\n",
" results = [be for be in bes if be.name == name]\n",
" \n",
" if len(results) == 1:\n",
" return results[0]\n",
" if len(results) > 1:\n",
" raise ValueError(\"Found more than one result for the query\")\n",
" if len(results) == 0:\n",
" raise KeyError(f\"The bloq example {name} was not found\") "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0d01d939-fa0d-4934-8a56-24f1087cd232",
"metadata": {},
"outputs": [],
"source": [
"from qualtran.drawing import show_call_graph\n",
"be = get_bloq_example('hubbard_time_evolution_by_gqsp')\n",
"bloq = be.make()\n",
"show_call_graph(bloq, max_depth=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e402cd50-8015-4f14-9c6f-1a7755bf4936",
"metadata": {},
"outputs": [],
"source": [
"%%timeit\n",
"from qualtran.resource_counting import get_cost_value, QubitCount\n",
"\n",
"get_cost_value(bloq, QubitCount())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "56391b2c-9381-480e-b204-e881fe0828ae",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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