example_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ibmqfactory.load_account:WARNING:2023-11-01 22:07:39,574: Credentials are already in use. The existing account in the session will be replaced.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AccountProvider for IBMQ(hub='ibm-q', group='open', project='main')>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Running All Imports\n",
    "\n",
    "import numpy as np\n",
    "from numpy import ndarray\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from qiskit import IBMQ, pulse, schedule\n",
    "\n",
    "from qiskit.circuit import Parameter, QuantumCircuit, Gate\n",
    "from qiskit.tools.monitor import job_monitor\n",
    "\n",
    "from qiskit.visualization.pulse_v2 import stylesheet\n",
    "\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "from typing import Optional, Union\n",
    "\n",
    "IBMQ.load_account()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<IBMQSimulator('ibmq_qasm_simulator') from IBMQ(hub='ibm-q-nus', group='default', project='default')>,\n",
       " <IBMQBackend('ibmq_kolkata') from IBMQ(hub='ibm-q-nus', group='default', project='default')>,\n",
       " <IBMQBackend('ibmq_mumbai') from IBMQ(hub='ibm-q-nus', group='default', project='default')>,\n",
       " <IBMQSimulator('simulator_statevector') from IBMQ(hub='ibm-q-nus', group='default', project='default')>,\n",
       " <IBMQSimulator('simulator_mps') from IBMQ(hub='ibm-q-nus', group='default', project='default')>,\n",
       " <IBMQSimulator('simulator_extended_stabilizer') from IBMQ(hub='ibm-q-nus', group='default', project='default')>,\n",
       " <IBMQSimulator('simulator_stabilizer') from IBMQ(hub='ibm-q-nus', group='default', project='default')>,\n",
       " <IBMQBackend('ibm_hanoi') from IBMQ(hub='ibm-q-nus', group='default', project='default')>,\n",
       " <IBMQBackend('ibm_algiers') from IBMQ(hub='ibm-q-nus', group='default', project='default')>,\n",
       " <IBMQBackend('ibm_lagos') from IBMQ(hub='ibm-q-nus', group='default', project='default')>,\n",
       " <IBMQBackend('ibm_nairobi') from IBMQ(hub='ibm-q-nus', group='default', project='default')>,\n",
       " <IBMQBackend('ibm_cairo') from IBMQ(hub='ibm-q-nus', group='default', project='default')>,\n",
       " <IBMQBackend('ibm_auckland') from IBMQ(hub='ibm-q-nus', group='default', project='default')>,\n",
       " <IBMQBackend('ibm_perth') from IBMQ(hub='ibm-q-nus', group='default', project='default')>,\n",
       " <IBMQBackend('ibm_sherbrooke') from IBMQ(hub='ibm-q-nus', group='default', project='default')>,\n",
       " <IBMQBackend('ibm_brisbane') from IBMQ(hub='ibm-q-nus', group='default', project='default')>,\n",
       " <IBMQBackend('ibm_nazca') from IBMQ(hub='ibm-q-nus', group='default', project='default')>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "provider = IBMQ.get_provider(hub='ibm-q-nus', group='default', project='default')\n",
    "provider.backends()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "backend_name\n",
      "ibm_cairo\n",
      "backend_version\n",
      "1.3.5\n",
      "n_qubits\n",
      "27\n",
      "basis_gates\n",
      "['id', 'rz', 'sx', 'x', 'cx', 'reset']\n",
      "gates\n",
      "[{'name': 'id', 'parameters': [], 'qasm_def': 'gate id q { U(0, 0, 0) q; }', 'coupling_map': [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26]]}, {'name': 'rz', 'parameters': ['theta'], 'qasm_def': 'gate rz(theta) q { U(0, 0, theta) q; }', 'coupling_map': [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26]]}, {'name': 'sx', 'parameters': [], 'qasm_def': 'gate sx q { U(pi/2, 3*pi/2, pi/2) q; }', 'coupling_map': [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26]]}, {'name': 'x', 'parameters': [], 'qasm_def': 'gate x q { U(pi, 0, pi) q; }', 'coupling_map': [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26]]}, {'name': 'cx', 'parameters': [], 'qasm_def': 'gate cx q0, q1 { CX q0, q1; }', 'coupling_map': [[0, 1], [1, 0], [1, 2], [1, 4], [2, 1], [2, 3], [3, 2], [3, 5], [4, 1], [4, 7], [5, 3], [5, 8], [6, 7], [7, 4], [7, 6], [7, 10], [8, 5], [8, 9], [8, 11], [9, 8], [10, 7], [10, 12], [11, 8], [11, 14], [12, 10], [12, 13], [12, 15], [13, 12], [13, 14], [14, 11], [14, 13], [14, 16], [15, 12], [15, 18], [16, 14], [16, 19], [17, 18], [18, 15], [18, 17], [18, 21], [19, 16], [19, 20], [19, 22], [20, 19], [21, 18], [21, 23], [22, 19], [22, 25], [23, 21], [23, 24], [24, 23], [24, 25], [25, 22], [25, 24], [25, 26], [26, 25]]}, {'name': 'reset', 'parameters': None, 'qasm_def': None}]\n",
      "local\n",
      "False\n",
      "simulator\n",
      "False\n",
      "conditional\n",
      "False\n",
      "open_pulse\n",
      "True\n",
      "memory\n",
      "True\n",
      "max_shots\n",
      "100000\n",
      "coupling_map\n",
      "[[0, 1], [1, 0], [1, 2], [1, 4], [2, 1], [2, 3], [3, 2], [3, 5], [4, 1], [4, 7], [5, 3], [5, 8], [6, 7], [7, 4], [7, 6], [7, 10], [8, 5], [8, 9], [8, 11], [9, 8], [10, 7], [10, 12], [11, 8], [11, 14], [12, 10], [12, 13], [12, 15], [13, 12], [13, 14], [14, 11], [14, 13], [14, 16], [15, 12], [15, 18], [16, 14], [16, 19], [17, 18], [18, 15], [18, 17], [18, 21], [19, 16], [19, 20], [19, 22], [20, 19], [21, 18], [21, 23], [22, 19], [22, 25], [23, 21], [23, 24], [24, 23], [24, 25], [25, 22], [25, 24], [25, 26], [26, 25]]\n",
      "dynamic_reprate_enabled\n",
      "True\n",
      "supported_instructions\n",
      "['cx', 'id', 'delay', 'measure', 'reset', 'rz', 'sx', 'x', 'if_else', 'for_loop', 'switch_case']\n",
      "rep_delay_range\n",
      "[0.0, 500.0]\n",
      "default_rep_delay\n",
      "250.0\n",
      "max_experiments\n",
      "300\n",
      "sample_name\n",
      "family: Falcon, revision: 5.11\n",
      "n_registers\n",
      "1\n",
      "credits_required\n",
      "True\n",
      "online_date\n",
      "2021-05-20 04:00:00+00:00\n",
      "description\n",
      "27 qubit device\n",
      "dt\n",
      "0.2222222222222222\n",
      "dtm\n",
      "0.2222222222222222\n",
      "processor_type\n",
      "{'family': 'Falcon', 'revision': '5.11'}\n",
      "parametric_pulses\n",
      "['gaussian', 'gaussian_square', 'gaussian_square_drag', 'drag', 'constant']\n",
      "allow_q_object\n",
      "True\n",
      "clops\n",
      "2440\n",
      "measure_esp_enabled\n",
      "True\n",
      "multi_meas_enabled\n",
      "True\n",
      "parallel_compilation\n",
      "True\n",
      "quantum_volume\n",
      "64\n",
      "qubit_channel_mapping\n",
      "[['u0', 'd0', 'm0', 'u1'], ['u0', 'd1', 'u1', 'u2', 'u8', 'u3', 'u4', 'm1'], ['u5', 'm2', 'u4', 'u2', 'u6', 'd2'], ['u5', 'u6', 'u7', 'd3', 'u10', 'm3'], ['d4', 'u13', 'm4', 'u8', 'u3', 'u9'], ['u16', 'u10', 'm5', 'u7', 'u11', 'd5'], ['m6', 'u14', 'd6', 'u12'], ['u13', 'd7', 'u14', 'm7', 'u20', 'u9', 'u15', 'u12'], ['u16', 'u17', 'u22', 'u19', 'm8', 'u18', 'd8', 'u11'], ['m9', 'd9', 'u19', 'u17'], ['u21', 'm10', 'd10', 'u24', 'u20', 'u15'], ['u29', 'd11', 'u22', 'u23', 'u18', 'm11'], ['u21', 'm12', 'u24', 'u25', 'u26', 'd12', 'u32', 'u27'], ['u25', 'm13', 'u30', 'u28', 'd13', 'u27'], ['m14', 'u29', 'u31', 'u34', 'u30', 'u28', 'u23', 'd14'], ['d15', 'm15', 'u33', 'u26', 'u32', 'u37'], ['u31', 'u35', 'u34', 'u40', 'm16', 'd16'], ['d17', 'u36', 'm17', 'u38'], ['u33', 'u36', 'u39', 'm18', 'u44', 'd18', 'u38', 'u37'], ['u35', 'u43', 'u41', 'u46', 'u40', 'd19', 'm19', 'u42'], ['u43', 'd20', 'u41', 'm20'], ['u48', 'u45', 'u39', 'd21', 'u44', 'm21'], ['m22', 'u52', 'u46', 'u47', 'd22', 'u42'], ['u48', 'u49', 'u50', 'd23', 'u45', 'm23'], ['u53', 'd24', 'u49', 'u51', 'u50', 'm24'], ['u53', 'u51', 'u52', 'm25', 'u47', 'u55', 'u54', 'd25'], ['m26', 'u54', 'd26', 'u55']]\n",
      "supported_features\n",
      "['qobj', 'qasm3']\n",
      "timing_constraints\n",
      "{'acquire_alignment': 16, 'granularity': 16, 'min_length': 64, 'pulse_alignment': 16}\n",
      "uchannels_enabled\n",
      "True\n",
      "url\n",
      "None\n",
      "input_allowed\n",
      "['job', 'runtime']\n",
      "allow_object_storage\n",
      "True\n",
      "pulse_num_channels\n",
      "9\n",
      "pulse_num_qubits\n",
      "3\n",
      "live_data\n",
      "False\n",
      "n_uchannels\n",
      "56\n",
      "u_channel_lo\n",
      "[[{'q': 1, 'scale': (1+0j)}], [{'q': 0, 'scale': (1+0j)}], [{'q': 2, 'scale': (1+0j)}], [{'q': 4, 'scale': (1+0j)}], [{'q': 1, 'scale': (1+0j)}], [{'q': 3, 'scale': (1+0j)}], [{'q': 2, 'scale': (1+0j)}], [{'q': 5, 'scale': (1+0j)}], [{'q': 1, 'scale': (1+0j)}], [{'q': 7, 'scale': (1+0j)}], [{'q': 3, 'scale': (1+0j)}], [{'q': 8, 'scale': (1+0j)}], [{'q': 7, 'scale': (1+0j)}], [{'q': 4, 'scale': (1+0j)}], [{'q': 6, 'scale': (1+0j)}], [{'q': 10, 'scale': (1+0j)}], [{'q': 5, 'scale': (1+0j)}], [{'q': 9, 'scale': (1+0j)}], [{'q': 11, 'scale': (1+0j)}], [{'q': 8, 'scale': (1+0j)}], [{'q': 7, 'scale': (1+0j)}], [{'q': 12, 'scale': (1+0j)}], [{'q': 8, 'scale': (1+0j)}], [{'q': 14, 'scale': (1+0j)}], [{'q': 10, 'scale': (1+0j)}], [{'q': 13, 'scale': (1+0j)}], [{'q': 15, 'scale': (1+0j)}], [{'q': 12, 'scale': (1+0j)}], [{'q': 14, 'scale': (1+0j)}], [{'q': 11, 'scale': (1+0j)}], [{'q': 13, 'scale': (1+0j)}], [{'q': 16, 'scale': (1+0j)}], [{'q': 12, 'scale': (1+0j)}], [{'q': 18, 'scale': (1+0j)}], [{'q': 14, 'scale': (1+0j)}], [{'q': 19, 'scale': (1+0j)}], [{'q': 18, 'scale': (1+0j)}], [{'q': 15, 'scale': (1+0j)}], [{'q': 17, 'scale': (1+0j)}], [{'q': 21, 'scale': (1+0j)}], [{'q': 16, 'scale': (1+0j)}], [{'q': 20, 'scale': (1+0j)}], [{'q': 22, 'scale': (1+0j)}], [{'q': 19, 'scale': (1+0j)}], [{'q': 18, 'scale': (1+0j)}], [{'q': 23, 'scale': (1+0j)}], [{'q': 19, 'scale': (1+0j)}], [{'q': 25, 'scale': (1+0j)}], [{'q': 21, 'scale': (1+0j)}], [{'q': 24, 'scale': (1+0j)}], [{'q': 23, 'scale': (1+0j)}], [{'q': 25, 'scale': (1+0j)}], [{'q': 22, 'scale': (1+0j)}], [{'q': 24, 'scale': (1+0j)}], [{'q': 26, 'scale': (1+0j)}], [{'q': 25, 'scale': (1+0j)}]]\n",
      "meas_levels\n",
      "[1, 2]\n",
      "qubit_lo_range\n",
      "[[4.757028447416521, 5.757028447416521], [4.535771613936004, 5.535771613936004], [4.411049486113911, 5.411049486113911], [4.618551878540684, 5.618551878540684], [4.685655814832658, 5.685655814832658], [4.546091989506303, 5.546091989506303], [4.5988858755257676, 5.5988858755257676], [4.548024976086412, 5.548024976086412], [4.469042378351226, 5.469042378351226], [4.727468721768282, 5.727468721768282], [4.733511645425801, 5.733511645425801], [4.632690150973655, 5.632690150973655], [4.614926253259652, 5.614926253259653], [4.782066543465278, 5.782066543465278], [4.543653465765976, 5.543653465765976], [4.46261082369583, 5.46261082369583], [4.7769018318921095, 5.7769018318921095], [4.762122746629746, 5.762122746629746], [4.68596898616162, 5.68596898616162], [4.4782090200308415, 5.4782090200308415], [4.697114119381423, 5.697114119381423], [4.784419852686964, 5.784419852686964], [4.70022970967232, 5.700229709672321], [4.602369511828087, 5.602369511828087], [4.67671722404226, 5.67671722404226], [4.453741752842849, 5.453741752842849], [4.692016289452893, 5.692016289452893]]\n",
      "meas_lo_range\n",
      "[[6.664278061, 7.664278061], [6.777820818, 7.777820818], [6.646402984000001, 7.646402984000001], [6.776165242, 7.776165242], [6.721879428, 7.721879428], [6.724591836, 7.724591836], [6.849729881, 7.849729881000001], [6.602229398, 7.602229398], [6.595949986000001, 7.595949986000001], [6.816872401, 7.816872401], [6.669357802, 7.669357802], [6.730074147000001, 7.730074147000001], [6.841395111000001, 7.841395111000001], [6.733799605000001, 7.733799605000001], [6.777881079, 7.777881079], [6.795548516, 7.795548516], [6.662748915000001, 7.662748915000001], [6.862033566, 7.862033566000001], [6.608047229, 7.608047229], [6.594864102000001, 7.594864102000001], [6.840076547000001, 7.840076547000001], [6.750227541, 7.750227541], [6.732124423, 7.732124423], [6.812355419, 7.812355419], [6.682368886000001, 7.682368886000001], [6.7827208510000006, 7.7827208510000006], [6.662892279, 7.662892279]]\n",
      "meas_kernels\n",
      "['hw_qmfk']\n",
      "discriminators\n",
      "['hw_qmfk', 'quadratic_discriminator', 'linear_discriminator']\n",
      "rep_times\n",
      "[1000.0]\n",
      "meas_map\n",
      "[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]]\n",
      "acquisition_latency\n",
      "[]\n",
      "conditional_latency\n",
      "[]\n",
      "hamiltonian\n",
      "{'description': 'Qubits are modeled as Duffing oscillators. In this case, the system includes higher energy states, i.e. not just |0> and |1>. The Pauli operators are generalized via the following set of transformations:\\n\\n$(\\\\mathbb{I}-\\\\sigma_{i}^z)/2 \\\\rightarrow O_i \\\\equiv b^\\\\dagger_{i} b_{i}$,\\n\\n$\\\\sigma_{+} \\\\rightarrow b^\\\\dagger$,\\n\\n$\\\\sigma_{-} \\\\rightarrow b$,\\n\\n$\\\\sigma_{i}^X \\\\rightarrow b^\\\\dagger_{i} + b_{i}$.\\n\\nQubits are coupled through resonator buses. The provided Hamiltonian has been projected into the zero excitation subspace of the resonator buses leading to an effective qubit-qubit flip-flop interaction. The qubit resonance frequencies in the Hamiltonian are the cavity dressed frequencies and not exactly what is returned by the backend defaults, which also includes the dressing due to the qubit-qubit interactions.\\n\\nQuantities are returned in angular frequencies, with units 2*pi*GHz.\\n\\nWARNING: Currently not all system Hamiltonian information is available to the public, missing values have been replaced with 0.\\n', 'h_latex': '\\\\begin{align} \\\\mathcal{H}/\\\\hbar = & \\\\sum_{i=0}^{26}\\\\left(\\\\frac{\\\\omega_{q,i}}{2}(\\\\mathbb{I}-\\\\sigma_i^{z})+\\\\frac{\\\\Delta_{i}}{2}(O_i^2-O_i)+\\\\Omega_{d,i}D_i(t)\\\\sigma_i^{X}\\\\right) \\\\\\\\ & + J_{12,13}(\\\\sigma_{12}^{+}\\\\sigma_{13}^{-}+\\\\sigma_{12}^{-}\\\\sigma_{13}^{+}) + J_{14,16}(\\\\sigma_{14}^{+}\\\\sigma_{16}^{-}+\\\\sigma_{14}^{-}\\\\sigma_{16}^{+}) + J_{8,9}(\\\\sigma_{8}^{+}\\\\sigma_{9}^{-}+\\\\sigma_{8}^{-}\\\\sigma_{9}^{+}) + J_{17,18}(\\\\sigma_{17}^{+}\\\\sigma_{18}^{-}+\\\\sigma_{17}^{-}\\\\sigma_{18}^{+}) \\\\\\\\ & + J_{11,14}(\\\\sigma_{11}^{+}\\\\sigma_{14}^{-}+\\\\sigma_{11}^{-}\\\\sigma_{14}^{+}) + J_{10,12}(\\\\sigma_{10}^{+}\\\\sigma_{12}^{-}+\\\\sigma_{10}^{-}\\\\sigma_{12}^{+}) + J_{13,14}(\\\\sigma_{13}^{+}\\\\sigma_{14}^{-}+\\\\sigma_{13}^{-}\\\\sigma_{14}^{+}) + J_{7,10}(\\\\sigma_{7}^{+}\\\\sigma_{10}^{-}+\\\\sigma_{7}^{-}\\\\sigma_{10}^{+}) \\\\\\\\ & + J_{16,19}(\\\\sigma_{16}^{+}\\\\sigma_{19}^{-}+\\\\sigma_{16}^{-}\\\\sigma_{19}^{+}) + J_{12,15}(\\\\sigma_{12}^{+}\\\\sigma_{15}^{-}+\\\\sigma_{12}^{-}\\\\sigma_{15}^{+}) + J_{22,25}(\\\\sigma_{22}^{+}\\\\sigma_{25}^{-}+\\\\sigma_{22}^{-}\\\\sigma_{25}^{+}) + J_{23,24}(\\\\sigma_{23}^{+}\\\\sigma_{24}^{-}+\\\\sigma_{23}^{-}\\\\sigma_{24}^{+}) \\\\\\\\ & + J_{8,11}(\\\\sigma_{8}^{+}\\\\sigma_{11}^{-}+\\\\sigma_{8}^{-}\\\\sigma_{11}^{+}) + J_{0,1}(\\\\sigma_{0}^{+}\\\\sigma_{1}^{-}+\\\\sigma_{0}^{-}\\\\sigma_{1}^{+}) + J_{1,2}(\\\\sigma_{1}^{+}\\\\sigma_{2}^{-}+\\\\sigma_{1}^{-}\\\\sigma_{2}^{+}) + J_{19,20}(\\\\sigma_{19}^{+}\\\\sigma_{20}^{-}+\\\\sigma_{19}^{-}\\\\sigma_{20}^{+}) \\\\\\\\ & + J_{6,7}(\\\\sigma_{6}^{+}\\\\sigma_{7}^{-}+\\\\sigma_{6}^{-}\\\\sigma_{7}^{+}) + J_{24,25}(\\\\sigma_{24}^{+}\\\\sigma_{25}^{-}+\\\\sigma_{24}^{-}\\\\sigma_{25}^{+}) + J_{18,21}(\\\\sigma_{18}^{+}\\\\sigma_{21}^{-}+\\\\sigma_{18}^{-}\\\\sigma_{21}^{+}) + J_{4,7}(\\\\sigma_{4}^{+}\\\\sigma_{7}^{-}+\\\\sigma_{4}^{-}\\\\sigma_{7}^{+}) \\\\\\\\ & + J_{3,5}(\\\\sigma_{3}^{+}\\\\sigma_{5}^{-}+\\\\sigma_{3}^{-}\\\\sigma_{5}^{+}) + J_{21,23}(\\\\sigma_{21}^{+}\\\\sigma_{23}^{-}+\\\\sigma_{21}^{-}\\\\sigma_{23}^{+}) + J_{5,8}(\\\\sigma_{5}^{+}\\\\sigma_{8}^{-}+\\\\sigma_{5}^{-}\\\\sigma_{8}^{+}) + J_{1,4}(\\\\sigma_{1}^{+}\\\\sigma_{4}^{-}+\\\\sigma_{1}^{-}\\\\sigma_{4}^{+}) \\\\\\\\ & + J_{2,3}(\\\\sigma_{2}^{+}\\\\sigma_{3}^{-}+\\\\sigma_{2}^{-}\\\\sigma_{3}^{+}) + J_{19,22}(\\\\sigma_{19}^{+}\\\\sigma_{22}^{-}+\\\\sigma_{19}^{-}\\\\sigma_{22}^{+}) + J_{15,18}(\\\\sigma_{15}^{+}\\\\sigma_{18}^{-}+\\\\sigma_{15}^{-}\\\\sigma_{18}^{+}) + J_{25,26}(\\\\sigma_{25}^{+}\\\\sigma_{26}^{-}+\\\\sigma_{25}^{-}\\\\sigma_{26}^{+}) \\\\\\\\ & + \\\\Omega_{d,0}(U_{0}^{(0,1)}(t))\\\\sigma_{0}^{X} + \\\\Omega_{d,1}(U_{1}^{(1,0)}(t)+U_{3}^{(1,4)}(t)+U_{2}^{(1,2)}(t))\\\\sigma_{1}^{X} \\\\\\\\ & + \\\\Omega_{d,2}(U_{5}^{(2,3)}(t)+U_{4}^{(2,1)}(t))\\\\sigma_{2}^{X} + \\\\Omega_{d,3}(U_{6}^{(3,2)}(t)+U_{7}^{(3,5)}(t))\\\\sigma_{3}^{X} \\\\\\\\ & + \\\\Omega_{d,4}(U_{9}^{(4,7)}(t)+U_{8}^{(4,1)}(t))\\\\sigma_{4}^{X} + \\\\Omega_{d,5}(U_{11}^{(5,8)}(t)+U_{10}^{(5,3)}(t))\\\\sigma_{5}^{X} \\\\\\\\ & + \\\\Omega_{d,6}(U_{12}^{(6,7)}(t))\\\\sigma_{6}^{X} + \\\\Omega_{d,7}(U_{14}^{(7,6)}(t)+U_{13}^{(7,4)}(t)+U_{15}^{(7,10)}(t))\\\\sigma_{7}^{X} \\\\\\\\ & + \\\\Omega_{d,8}(U_{18}^{(8,11)}(t)+U_{16}^{(8,5)}(t)+U_{17}^{(8,9)}(t))\\\\sigma_{8}^{X} + \\\\Omega_{d,9}(U_{19}^{(9,8)}(t))\\\\sigma_{9}^{X} \\\\\\\\ & + \\\\Omega_{d,10}(U_{20}^{(10,7)}(t)+U_{21}^{(10,12)}(t))\\\\sigma_{10}^{X} + \\\\Omega_{d,11}(U_{22}^{(11,8)}(t)+U_{23}^{(11,14)}(t))\\\\sigma_{11}^{X} \\\\\\\\ & + \\\\Omega_{d,12}(U_{24}^{(12,10)}(t)+U_{25}^{(12,13)}(t)+U_{26}^{(12,15)}(t))\\\\sigma_{12}^{X} + \\\\Omega_{d,13}(U_{28}^{(13,14)}(t)+U_{27}^{(13,12)}(t))\\\\sigma_{13}^{X} \\\\\\\\ & + \\\\Omega_{d,14}(U_{31}^{(14,16)}(t)+U_{30}^{(14,13)}(t)+U_{29}^{(14,11)}(t))\\\\sigma_{14}^{X} + \\\\Omega_{d,15}(U_{33}^{(15,18)}(t)+U_{32}^{(15,12)}(t))\\\\sigma_{15}^{X} \\\\\\\\ & + \\\\Omega_{d,16}(U_{35}^{(16,19)}(t)+U_{34}^{(16,14)}(t))\\\\sigma_{16}^{X} + \\\\Omega_{d,17}(U_{36}^{(17,18)}(t))\\\\sigma_{17}^{X} \\\\\\\\ & + \\\\Omega_{d,18}(U_{37}^{(18,15)}(t)+U_{39}^{(18,21)}(t)+U_{38}^{(18,17)}(t))\\\\sigma_{18}^{X} + \\\\Omega_{d,19}(U_{42}^{(19,22)}(t)+U_{40}^{(19,16)}(t)+U_{41}^{(19,20)}(t))\\\\sigma_{19}^{X} \\\\\\\\ & + \\\\Omega_{d,20}(U_{43}^{(20,19)}(t))\\\\sigma_{20}^{X} + \\\\Omega_{d,21}(U_{44}^{(21,18)}(t)+U_{45}^{(21,23)}(t))\\\\sigma_{21}^{X} \\\\\\\\ & + \\\\Omega_{d,22}(U_{46}^{(22,19)}(t)+U_{47}^{(22,25)}(t))\\\\sigma_{22}^{X} + \\\\Omega_{d,23}(U_{49}^{(23,24)}(t)+U_{48}^{(23,21)}(t))\\\\sigma_{23}^{X} \\\\\\\\ & + \\\\Omega_{d,24}(U_{50}^{(24,23)}(t)+U_{51}^{(24,25)}(t))\\\\sigma_{24}^{X} + \\\\Omega_{d,25}(U_{54}^{(25,26)}(t)+U_{53}^{(25,24)}(t)+U_{52}^{(25,22)}(t))\\\\sigma_{25}^{X} \\\\\\\\ & + \\\\Omega_{d,26}(U_{55}^{(26,25)}(t))\\\\sigma_{26}^{X} \\\\\\\\ \\\\end{align}', 'h_str': ['_SUM[i,0,26,wq{i}/2*(I{i}-Z{i})]', '_SUM[i,0,26,delta{i}/2*O{i}*O{i}]', '_SUM[i,0,26,-delta{i}/2*O{i}]', '_SUM[i,0,26,omegad{i}*X{i}||D{i}]', 'jq12q13*Sp12*Sm13', 'jq12q13*Sm12*Sp13', 'jq14q16*Sp14*Sm16', 'jq14q16*Sm14*Sp16', 'jq8q9*Sp8*Sm9', 'jq8q9*Sm8*Sp9', 'jq17q18*Sp17*Sm18', 'jq17q18*Sm17*Sp18', 'jq11q14*Sp11*Sm14', 'jq11q14*Sm11*Sp14', 'jq10q12*Sp10*Sm12', 'jq10q12*Sm10*Sp12', 'jq13q14*Sp13*Sm14', 'jq13q14*Sm13*Sp14', 'jq7q10*Sp7*Sm10', 'jq7q10*Sm7*Sp10', 'jq16q19*Sp16*Sm19', 'jq16q19*Sm16*Sp19', 'jq12q15*Sp12*Sm15', 'jq12q15*Sm12*Sp15', 'jq22q25*Sp22*Sm25', 'jq22q25*Sm22*Sp25', 'jq23q24*Sp23*Sm24', 'jq23q24*Sm23*Sp24', 'jq8q11*Sp8*Sm11', 'jq8q11*Sm8*Sp11', 'jq0q1*Sp0*Sm1', 'jq0q1*Sm0*Sp1', 'jq1q2*Sp1*Sm2', 'jq1q2*Sm1*Sp2', 'jq19q20*Sp19*Sm20', 'jq19q20*Sm19*Sp20', 'jq6q7*Sp6*Sm7', 'jq6q7*Sm6*Sp7', 'jq24q25*Sp24*Sm25', 'jq24q25*Sm24*Sp25', 'jq18q21*Sp18*Sm21', 'jq18q21*Sm18*Sp21', 'jq4q7*Sp4*Sm7', 'jq4q7*Sm4*Sp7', 'jq3q5*Sp3*Sm5', 'jq3q5*Sm3*Sp5', 'jq21q23*Sp21*Sm23', 'jq21q23*Sm21*Sp23', 'jq5q8*Sp5*Sm8', 'jq5q8*Sm5*Sp8', 'jq1q4*Sp1*Sm4', 'jq1q4*Sm1*Sp4', 'jq2q3*Sp2*Sm3', 'jq2q3*Sm2*Sp3', 'jq19q22*Sp19*Sm22', 'jq19q22*Sm19*Sp22', 'jq15q18*Sp15*Sm18', 'jq15q18*Sm15*Sp18', 'jq25q26*Sp25*Sm26', 'jq25q26*Sm25*Sp26', 'omegad1*X0||U0', 'omegad0*X1||U1', 'omegad4*X1||U3', 'omegad2*X1||U2', 'omegad3*X2||U5', 'omegad1*X2||U4', 'omegad2*X3||U6', 'omegad5*X3||U7', 'omegad7*X4||U9', 'omegad1*X4||U8', 'omegad8*X5||U11', 'omegad3*X5||U10', 'omegad7*X6||U12', 'omegad6*X7||U14', 'omegad4*X7||U13', 'omegad10*X7||U15', 'omegad11*X8||U18', 'omegad5*X8||U16', 'omegad9*X8||U17', 'omegad8*X9||U19', 'omegad7*X10||U20', 'omegad12*X10||U21', 'omegad8*X11||U22', 'omegad14*X11||U23', 'omegad10*X12||U24', 'omegad13*X12||U25', 'omegad15*X12||U26', 'omegad14*X13||U28', 'omegad12*X13||U27', 'omegad16*X14||U31', 'omegad13*X14||U30', 'omegad11*X14||U29', 'omegad18*X15||U33', 'omegad12*X15||U32', 'omegad19*X16||U35', 'omegad14*X16||U34', 'omegad18*X17||U36', 'omegad15*X18||U37', 'omegad21*X18||U39', 'omegad17*X18||U38', 'omegad22*X19||U42', 'omegad16*X19||U40', 'omegad20*X19||U41', 'omegad19*X20||U43', 'omegad18*X21||U44', 'omegad23*X21||U45', 'omegad19*X22||U46', 'omegad25*X22||U47', 'omegad24*X23||U49', 'omegad21*X23||U48', 'omegad23*X24||U50', 'omegad25*X24||U51', 'omegad26*X25||U54', 'omegad24*X25||U53', 'omegad22*X25||U52', 'omegad25*X26||U55'], 'osc': {}, 'qub': {'0': 3, '1': 3, '2': 3, '3': 3, '4': 3, '5': 3, '6': 3, '7': 3, '8': 3, '9': 3, '10': 3, '11': 3, '12': 3, '13': 3, '14': 3, '15': 3, '16': 3, '17': 3, '18': 3, '19': 3, '20': 3, '21': 3, '22': 3, '23': 3, '24': 3, '25': 3, '26': 3}, 'vars': {'delta0': -2.121111627140078, 'delta1': -2.150521052228995, 'delta10': -2.131322304881302, 'delta11': -2.138450735904347, 'delta12': -2.1442919934822435, 'delta13': -2.3158089229728236, 'delta14': -2.1502437462786665, 'delta15': -2.1693664540924447, 'delta16': -2.1288428809993287, 'delta17': -2.1171684271563405, 'delta18': -2.1359889298950887, 'delta19': -2.1643007378123045, 'delta2': -2.170314361163121, 'delta20': -2.1347801652238227, 'delta21': -2.1262217544763624, 'delta22': -2.139343567360432, 'delta23': -2.1447283319628596, 'delta24': -2.137334889654229, 'delta25': -2.1653901448850346, 'delta26': -2.1259565647243566, 'delta3': -2.1386812430759723, 'delta4': -2.1317383149863844, 'delta5': -1.9144726160689196, 'delta6': -2.140272029839704, 'delta7': -2.146601841464824, 'delta8': -2.1624148560571967, 'delta9': -2.130356539471464, 'jq0q1': 0.012843456826016208, 'jq10q12': 0.012958178262129706, 'jq11q14': 0.011990235374496727, 'jq12q13': 0.013039494766158817, 'jq12q15': 0.011822832005884408, 'jq13q14': 0.013080585454088875, 'jq14q16': 0.012473493406261377, 'jq15q18': 0.01224142074614714, 'jq16q19': 0.012408455981837145, 'jq17q18': 0.013084055781744023, 'jq18q21': 0.01340460254077072, 'jq19q20': 0.012580797940535713, 'jq19q22': 0.012819187666246018, 'jq1q2': 0.012344845705212255, 'jq1q4': 0.012277946896886953, 'jq21q23': 0.013154316205196033, 'jq22q25': 0.012262179164754571, 'jq23q24': 0.012821752982633876, 'jq24q25': 0.012700822015345002, 'jq25q26': 0.012449297332342863, 'jq2q3': 0.012366251452906533, 'jq3q5': 0.012679958322937335, 'jq4q7': 0.01297927922044149, 'jq5q8': 0.01262797106230294, 'jq6q7': 0.012308097552500756, 'jq7q10': 0.012471225940589128, 'jq8q11': 0.012140189476625894, 'jq8q9': 0.012681826462095475, 'omegad0': 0.9488029317917932, 'omegad1': 0.70964216250958, 'omegad10': 0.7845996117033825, 'omegad11': 0.8421752115615377, 'omegad12': 0.7044583236778943, 'omegad13': 1.09776177351167, 'omegad14': 1.0177788480978625, 'omegad15': 0.8878349311454393, 'omegad16': 1.429662030887592, 'omegad17': 0.6121199612775846, 'omegad18': 0.6885172102892431, 'omegad19': 1.0613877258687092, 'omegad2': 0.8215638534639479, 'omegad20': 0.8760717217631845, 'omegad21': 0.4742509383347022, 'omegad22': 0.9254859811649753, 'omegad23': 0.6403468559836302, 'omegad24': 0.8616801605958918, 'omegad25': 0.7950696512860692, 'omegad26': 0.8611723717897554, 'omegad3': 0.8584980718106326, 'omegad4': 0.839898855742236, 'omegad5': 0.768964704998336, 'omegad6': 1.1476947508451434, 'omegad7': 0.8328943692301296, 'omegad8': 0.8282716412091762, 'omegad9': 1.8436376587434584, 'wq0': 33.03088390023259, 'wq1': 31.640686214994734, 'wq10': 32.88312347549265, 'wq11': 32.24964334290304, 'wq12': 32.13802948178818, 'wq13': 33.1882028974459, 'wq14': 31.690209350606185, 'wq15': 31.181003412696032, 'wq16': 33.155752057573544, 'wq17': 33.06289232619951, 'wq18': 32.5844041373397, 'wq19': 31.279009770726667, 'wq2': 30.857033973982787, 'wq20': 32.65443107463293, 'wq21': 33.202989175370845, 'wq22': 32.67400690577189, 'wq23': 32.059133148519315, 'wq24': 32.52627360152582, 'wq25': 31.125277397024245, 'wq26': 32.6224004645275, 'wq3': 32.1608099572833, 'wq4': 32.58243642384694, 'wq5': 31.70553104714261, 'wq6': 32.03724481608903, 'wq7': 31.717676360021734, 'wq8': 31.22141406240913, 'wq9': 32.84515466635532}}\n",
      "channels\n",
      "{'acquire0': {'operates': {'qubits': [0]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire1': {'operates': {'qubits': [1]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire10': {'operates': {'qubits': [10]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire11': {'operates': {'qubits': [11]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire12': {'operates': {'qubits': [12]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire13': {'operates': {'qubits': [13]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire14': {'operates': {'qubits': [14]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire15': {'operates': {'qubits': [15]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire16': {'operates': {'qubits': [16]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire17': {'operates': {'qubits': [17]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire18': {'operates': {'qubits': [18]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire19': {'operates': {'qubits': [19]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire2': {'operates': {'qubits': [2]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire20': {'operates': {'qubits': [20]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire21': {'operates': {'qubits': [21]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire22': {'operates': {'qubits': [22]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire23': {'operates': {'qubits': [23]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire24': {'operates': {'qubits': [24]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire25': {'operates': {'qubits': [25]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire26': {'operates': {'qubits': [26]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire3': {'operates': {'qubits': [3]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire4': {'operates': {'qubits': [4]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire5': {'operates': {'qubits': [5]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire6': {'operates': {'qubits': [6]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire7': {'operates': {'qubits': [7]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire8': {'operates': {'qubits': [8]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire9': {'operates': {'qubits': [9]}, 'purpose': 'acquire', 'type': 'acquire'}, 'd0': {'operates': {'qubits': [0]}, 'purpose': 'drive', 'type': 'drive'}, 'd1': {'operates': {'qubits': [1]}, 'purpose': 'drive', 'type': 'drive'}, 'd10': {'operates': {'qubits': [10]}, 'purpose': 'drive', 'type': 'drive'}, 'd11': {'operates': {'qubits': [11]}, 'purpose': 'drive', 'type': 'drive'}, 'd12': {'operates': {'qubits': [12]}, 'purpose': 'drive', 'type': 'drive'}, 'd13': {'operates': {'qubits': [13]}, 'purpose': 'drive', 'type': 'drive'}, 'd14': {'operates': {'qubits': [14]}, 'purpose': 'drive', 'type': 'drive'}, 'd15': {'operates': {'qubits': [15]}, 'purpose': 'drive', 'type': 'drive'}, 'd16': {'operates': {'qubits': [16]}, 'purpose': 'drive', 'type': 'drive'}, 'd17': {'operates': {'qubits': [17]}, 'purpose': 'drive', 'type': 'drive'}, 'd18': {'operates': {'qubits': [18]}, 'purpose': 'drive', 'type': 'drive'}, 'd19': {'operates': {'qubits': [19]}, 'purpose': 'drive', 'type': 'drive'}, 'd2': {'operates': {'qubits': [2]}, 'purpose': 'drive', 'type': 'drive'}, 'd20': {'operates': {'qubits': [20]}, 'purpose': 'drive', 'type': 'drive'}, 'd21': {'operates': {'qubits': [21]}, 'purpose': 'drive', 'type': 'drive'}, 'd22': {'operates': {'qubits': [22]}, 'purpose': 'drive', 'type': 'drive'}, 'd23': {'operates': {'qubits': [23]}, 'purpose': 'drive', 'type': 'drive'}, 'd24': {'operates': {'qubits': [24]}, 'purpose': 'drive', 'type': 'drive'}, 'd25': {'operates': {'qubits': [25]}, 'purpose': 'drive', 'type': 'drive'}, 'd26': {'operates': {'qubits': [26]}, 'purpose': 'drive', 'type': 'drive'}, 'd3': {'operates': {'qubits': [3]}, 'purpose': 'drive', 'type': 'drive'}, 'd4': {'operates': {'qubits': [4]}, 'purpose': 'drive', 'type': 'drive'}, 'd5': {'operates': {'qubits': [5]}, 'purpose': 'drive', 'type': 'drive'}, 'd6': {'operates': {'qubits': [6]}, 'purpose': 'drive', 'type': 'drive'}, 'd7': {'operates': {'qubits': [7]}, 'purpose': 'drive', 'type': 'drive'}, 'd8': {'operates': {'qubits': [8]}, 'purpose': 'drive', 'type': 'drive'}, 'd9': {'operates': {'qubits': [9]}, 'purpose': 'drive', 'type': 'drive'}, 'm0': {'operates': {'qubits': [0]}, 'purpose': 'measure', 'type': 'measure'}, 'm1': {'operates': {'qubits': [1]}, 'purpose': 'measure', 'type': 'measure'}, 'm10': {'operates': {'qubits': [10]}, 'purpose': 'measure', 'type': 'measure'}, 'm11': {'operates': {'qubits': [11]}, 'purpose': 'measure', 'type': 'measure'}, 'm12': {'operates': {'qubits': [12]}, 'purpose': 'measure', 'type': 'measure'}, 'm13': {'operates': {'qubits': [13]}, 'purpose': 'measure', 'type': 'measure'}, 'm14': {'operates': {'qubits': [14]}, 'purpose': 'measure', 'type': 'measure'}, 'm15': {'operates': {'qubits': [15]}, 'purpose': 'measure', 'type': 'measure'}, 'm16': {'operates': {'qubits': [16]}, 'purpose': 'measure', 'type': 'measure'}, 'm17': {'operates': {'qubits': [17]}, 'purpose': 'measure', 'type': 'measure'}, 'm18': {'operates': {'qubits': [18]}, 'purpose': 'measure', 'type': 'measure'}, 'm19': {'operates': {'qubits': [19]}, 'purpose': 'measure', 'type': 'measure'}, 'm2': {'operates': {'qubits': [2]}, 'purpose': 'measure', 'type': 'measure'}, 'm20': {'operates': {'qubits': [20]}, 'purpose': 'measure', 'type': 'measure'}, 'm21': {'operates': {'qubits': [21]}, 'purpose': 'measure', 'type': 'measure'}, 'm22': {'operates': {'qubits': [22]}, 'purpose': 'measure', 'type': 'measure'}, 'm23': {'operates': {'qubits': [23]}, 'purpose': 'measure', 'type': 'measure'}, 'm24': {'operates': {'qubits': [24]}, 'purpose': 'measure', 'type': 'measure'}, 'm25': {'operates': {'qubits': [25]}, 'purpose': 'measure', 'type': 'measure'}, 'm26': {'operates': {'qubits': [26]}, 'purpose': 'measure', 'type': 'measure'}, 'm3': {'operates': {'qubits': [3]}, 'purpose': 'measure', 'type': 'measure'}, 'm4': {'operates': {'qubits': [4]}, 'purpose': 'measure', 'type': 'measure'}, 'm5': {'operates': {'qubits': [5]}, 'purpose': 'measure', 'type': 'measure'}, 'm6': {'operates': {'qubits': [6]}, 'purpose': 'measure', 'type': 'measure'}, 'm7': {'operates': {'qubits': [7]}, 'purpose': 'measure', 'type': 'measure'}, 'm8': {'operates': {'qubits': [8]}, 'purpose': 'measure', 'type': 'measure'}, 'm9': {'operates': {'qubits': [9]}, 'purpose': 'measure', 'type': 'measure'}, 'u0': {'operates': {'qubits': [0, 1]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u1': {'operates': {'qubits': [1, 0]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u10': {'operates': {'qubits': [5, 3]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u11': {'operates': {'qubits': [5, 8]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u12': {'operates': {'qubits': [6, 7]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u13': {'operates': {'qubits': [7, 4]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u14': {'operates': {'qubits': [7, 6]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u15': {'operates': {'qubits': [7, 10]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u16': {'operates': {'qubits': [8, 5]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u17': {'operates': {'qubits': [8, 9]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u18': {'operates': {'qubits': [8, 11]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u19': {'operates': {'qubits': [9, 8]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u2': {'operates': {'qubits': [1, 2]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u20': {'operates': {'qubits': [10, 7]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u21': {'operates': {'qubits': [10, 12]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u22': {'operates': {'qubits': [11, 8]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u23': {'operates': {'qubits': [11, 14]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u24': {'operates': {'qubits': [12, 10]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u25': {'operates': {'qubits': [12, 13]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u26': {'operates': {'qubits': [12, 15]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u27': {'operates': {'qubits': [13, 12]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u28': {'operates': {'qubits': [13, 14]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u29': {'operates': {'qubits': [14, 11]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u3': {'operates': {'qubits': [1, 4]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u30': {'operates': {'qubits': [14, 13]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u31': {'operates': {'qubits': [14, 16]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u32': {'operates': {'qubits': [15, 12]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u33': {'operates': {'qubits': [15, 18]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u34': {'operates': {'qubits': [16, 14]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u35': {'operates': {'qubits': [16, 19]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u36': {'operates': {'qubits': [17, 18]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u37': {'operates': {'qubits': [18, 15]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u38': {'operates': {'qubits': [18, 17]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u39': {'operates': {'qubits': [18, 21]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u4': {'operates': {'qubits': [2, 1]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u40': {'operates': {'qubits': [19, 16]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u41': {'operates': {'qubits': [19, 20]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u42': {'operates': {'qubits': [19, 22]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u43': {'operates': {'qubits': [20, 19]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u44': {'operates': {'qubits': [21, 18]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u45': {'operates': {'qubits': [21, 23]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u46': {'operates': {'qubits': [22, 19]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u47': {'operates': {'qubits': [22, 25]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u48': {'operates': {'qubits': [23, 21]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u49': {'operates': {'qubits': [23, 24]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u5': {'operates': {'qubits': [2, 3]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u50': {'operates': {'qubits': [24, 23]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u51': {'operates': {'qubits': [24, 25]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u52': {'operates': {'qubits': [25, 22]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u53': {'operates': {'qubits': [25, 24]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u54': {'operates': {'qubits': [25, 26]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u55': {'operates': {'qubits': [26, 25]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u6': {'operates': {'qubits': [3, 2]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u7': {'operates': {'qubits': [3, 5]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u8': {'operates': {'qubits': [4, 1]}, 'purpose': 'cross-resonance', 'type': 'control'}, 'u9': {'operates': {'qubits': [4, 7]}, 'purpose': 'cross-resonance', 'type': 'control'}}\n",
      "Sampling time: 0.2222222222222222 ns\n",
      "Least common multiple of acquire_alignment and pulse_alignment: 16\n",
      "Qubit 0 has an estimated frequency of 5.25702844741652 GHz.\n",
      "Qubit 1 has an estimated frequency of 5.035771613936004 GHz.\n",
      "Resonator for qubit 0 has an estimated frequency of 7.164278061 GHz.\n",
      "Resonator for qubit 1 has an estimated frequency of 7.277820818 GHz.\n",
      "measure_instructions: [[0 Acquire(1792, AcquireChannel(0), MemorySlot(0))]\n",
      " [0 Acquire(1792, AcquireChannel(1), MemorySlot(1))]\n",
      " [0 Acquire(1792, AcquireChannel(10), MemorySlot(10))]\n",
      " [0 Acquire(1792, AcquireChannel(11), MemorySlot(11))]\n",
      " [0 Acquire(1792, AcquireChannel(12), MemorySlot(12))]\n",
      " [0 Acquire(1792, AcquireChannel(13), MemorySlot(13))]\n",
      " [0 Acquire(1792, AcquireChannel(14), MemorySlot(14))]\n",
      " [0 Acquire(1792, AcquireChannel(15), MemorySlot(15))]\n",
      " [0 Acquire(1792, AcquireChannel(16), MemorySlot(16))]\n",
      " [0 Acquire(1792, AcquireChannel(17), MemorySlot(17))]\n",
      " [0 Acquire(1792, AcquireChannel(18), MemorySlot(18))]\n",
      " [0 Acquire(1792, AcquireChannel(19), MemorySlot(19))]\n",
      " [0 Acquire(1792, AcquireChannel(2), MemorySlot(2))]\n",
      " [0 Acquire(1792, AcquireChannel(20), MemorySlot(20))]\n",
      " [0 Acquire(1792, AcquireChannel(21), MemorySlot(21))]\n",
      " [0 Acquire(1792, AcquireChannel(22), MemorySlot(22))]\n",
      " [0 Acquire(1792, AcquireChannel(23), MemorySlot(23))]\n",
      " [0 Acquire(1792, AcquireChannel(24), MemorySlot(24))]\n",
      " [0 Acquire(1792, AcquireChannel(25), MemorySlot(25))]\n",
      " [0 Acquire(1792, AcquireChannel(26), MemorySlot(26))]\n",
      " [0 Acquire(1792, AcquireChannel(3), MemorySlot(3))]\n",
      " [0 Acquire(1792, AcquireChannel(4), MemorySlot(4))]\n",
      " [0 Acquire(1792, AcquireChannel(5), MemorySlot(5))]\n",
      " [0 Acquire(1792, AcquireChannel(6), MemorySlot(6))]\n",
      " [0 Acquire(1792, AcquireChannel(7), MemorySlot(7))]\n",
      " [0 Acquire(1792, AcquireChannel(8), MemorySlot(8))]\n",
      " [0 Acquire(1792, AcquireChannel(9), MemorySlot(9))]\n",
      " [0\n",
      "  Play(GaussianSquare(duration=1792, sigma=64, width=1536, amp=0.42944000000000004, angle=-0.3569276790158107, name='M_m0'), MeasureChannel(0), name='M_m0')]\n",
      " [1792 Delay(1504, MeasureChannel(0))]]\n",
      "measurement pulse: GaussianSquare(duration=1792, sigma=64, width=1536, amp=0.42944000000000004, angle=-0.3569276790158107, name='M_m0')\n",
      "measurement delay: Delay(1504, MeasureChannel(0))\n",
      "x pulse: Drag(duration=112, sigma=28, beta=0.3820417153271499, amp=0.2845167924241537, angle=0.0, name='Xp_d0')\n",
      "sx pulse: Drag(duration=112, sigma=28, beta=0.3720108541074172, amp=0.14189846514243712, angle=-0.010111452734135882, name='X90p_d0')\n"
     ]
    }
   ],
   "source": [
    "backend = provider.get_backend('ibm_cairo') # replace the name with the exact one for the backend that is desired from the previous displayed backends\n",
    "\n",
    "backend_config = backend.configuration()\n",
    "\n",
    "for key, value in backend_config.to_dict().items():\n",
    "    print(key)\n",
    "    print(value)\n",
    "\n",
    "assert backend_config.open_pulse, \"Backend doesn't support Pulse\"\n",
    "# Verifies whether Pulse is supported\n",
    "\n",
    "dt = backend_config.dt # Example way of getting data on the backend\n",
    "print(f\"Sampling time: {dt*1e9} ns\")    # The configuration returns dt in seconds, so multiply by\n",
    "                                        # 1e9 to get nanoseconds\n",
    "backend_defaults = backend.defaults() \n",
    "\n",
    "backend.configuration().timing_constraints\n",
    "\n",
    "acquire_alignment = backend.configuration().timing_constraints['acquire_alignment']\n",
    "granularity = backend.configuration().timing_constraints['granularity']\n",
    "pulse_alignment = backend.configuration().timing_constraints['pulse_alignment']\n",
    "\n",
    "lcm = np.lcm(acquire_alignment, pulse_alignment)\n",
    "print(f\"Least common multiple of acquire_alignment and pulse_alignment: {lcm}\")\n",
    "\n",
    "# unit conversion factors -> all backend properties returned in SI (Hz, sec, etc.)\n",
    "GHz = 1.0e9 # Gigahertz\n",
    "MHz = 1.0e6 # Megahertz\n",
    "us = 1.0e-6 # Microseconds\n",
    "ns = 1.0e-9 # Nanoseconds\n",
    "\n",
    "# Experiments will be run on these qubits\n",
    "qubit_0 = 0\n",
    "qubit_1 = 1\n",
    "\n",
    "# The sweep will be centered around the estimated qubit frequency.\n",
    "center_frequency_Hz_0 = backend_defaults.qubit_freq_est[qubit_0]\n",
    "center_frequency_Hz_1 = backend_defaults.qubit_freq_est[qubit_1]\n",
    "\n",
    "meas_freq_0 = backend_defaults.meas_freq_est[qubit_0]\n",
    "meas_freq_1 = backend_defaults.meas_freq_est[qubit_1]\n",
    "\n",
    "print(f\"Qubit {qubit_0} has an estimated frequency of {center_frequency_Hz_0 / GHz} GHz.\")\n",
    "print(f\"Qubit {qubit_1} has an estimated frequency of {center_frequency_Hz_1 / GHz} GHz.\")\n",
    "\n",
    "print(f\"Resonator for qubit {qubit_0} has an estimated frequency of {meas_freq_0 / GHz} GHz.\")\n",
    "print(f\"Resonator for qubit {qubit_1} has an estimated frequency of {meas_freq_1 / GHz} GHz.\")\n",
    "\n",
    "# scale factor to remove factors of 10 from the data\n",
    "scale_factor = 1e-7\n",
    "\n",
    "def get_closest_multiple_of(value, base_number):\n",
    "    return int(value + base_number/2) - (int(value + base_number/2) % base_number)\n",
    "\n",
    "# samples need to be multiples of 16\n",
    "def get_closest_multiple_of_16(num):\n",
    "    return get_closest_multiple_of(num, granularity)\n",
    "\n",
    "# Convert seconds to dt\n",
    "def get_dt_from(sec):\n",
    "    return get_closest_multiple_of(sec/dt, lcm)\n",
    "\n",
    "def fit_function(x_values, y_values, function, init_params):\n",
    "    fitparams, conv = curve_fit(function, x_values, y_values, init_params)\n",
    "    y_fit = function(x_values, *fitparams)\n",
    "    \n",
    "    return fitparams, y_fit\n",
    "\n",
    "# exponential function\n",
    "def exponential(t, tau, a, offset):\n",
    "    return a*np.exp(-t/tau)+offset\n",
    "\n",
    "# fit an exponential function\n",
    "def fit_exponential(ts, values):\n",
    "    p0=[np.average(ts), 1, 0]\n",
    "    return curve_fit(exponential, ts, values, p0=p0)\n",
    "\n",
    "qubit = qubit_0\n",
    "\n",
    "instruction_schedule_map = backend.defaults().instruction_schedule_map\n",
    "measure_instructions = np.array(instruction_schedule_map.get(\"measure\", qubits=[qubit]).instructions)\n",
    "measure_pulse = measure_instructions[-2,1].pulse\n",
    "measure_delay = measure_instructions[-1,1]\n",
    "\n",
    "print(f\"measure_instructions: {measure_instructions}\")\n",
    "\n",
    "x_instructions = np.array(instruction_schedule_map.get(\"x\", qubits=[qubit]).instructions)\n",
    "x_pulse = x_instructions[0,1].pulse\n",
    "\n",
    "sx_instructions = np.array(instruction_schedule_map.get(\"sx\", qubits=[qubit]).instructions)\n",
    "sx_pulse = sx_instructions[0,1].pulse\n",
    "\n",
    "print(f\"measurement pulse: {measure_pulse}\")\n",
    "print(f\"measurement delay: {measure_delay}\")\n",
    "print(f\"x pulse: {x_pulse}\")\n",
    "print(f\"sx pulse: {sx_pulse}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Experiments from Helper Packages\n",
    "\n",
    "from helper_funcs.helpers import integrated_fidelity_experiment, rr_freq_spec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "2\n"
     ]
    },
    {
     "data": {
      "image/png": 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22IKF+vXrG/369TPatGljuLm5GbVr1za2b9+e5XWlnSxLMkaPHm2kpqbapn344Ye20PV6LVu2NCQZ8+bNc1jfli1bjKioqCxvPyPpfY5FR0cb69evt723R44c6bDc77//7hAapqamGm+88YbtBPl6ly5dMoKDgw1JxnvvvWe3/4ZhGBs3bjQuXLhg+/+NQU1ERITRuHFjW2h9vb/++stwdXU1QkJCjG3bttlN++abbwxJxt13323XnpP3zZw5cwxJRtmyZe3CmAsXLhg1a9a0rS8/gpobAxSr1Wq88MILhiSjbt266a5LkjFu3DjbMdVqtRqDBw82JBk1atQwatasaXfcXr16dYaBcla99dZbtvXf+J69evWqQxC7cOFChwsjUVFRRteuXQ1JxnfffWc37WZBjSTj3Xffdahr3rx5tu8Rhw4dsqupefPmtvcpAADIOwQ1eez6oMYwDGP06NEOX3IyCmoykxZe7N6926497WT8m2++cVimZ8+ehiSjefPmDtMWLVrk8IUuOTnZCAoKMgoVKuRw0mEYhhEXF2eUKFHC8Pf3tzuxOH36tBEaGmqEhoZmeX9ee+0121XNG0VERBg+Pj6ZBjVff/21w3Lvv/++w+/16NGjhiSjSJEiDuGCYVy7Ep6doGbHjh2GJKNRo0YOJ1eGYRjbt283JBndu3fP0voyk9WgJiEhwfYl/Pq/260ENV26dMl2vXFxcbZeBVnZ1vXSOwF6+OGHDUnGiy++6DD/hQsXDG9vb8PFxcU4efKkrT3thKRZs2YOy0RERGR4ovXQQw8ZoaGhxvz58zPfyUxkJahJSUkxGjZsaAQGBhoREREOdWcU1BjGtTAg7Qp32k+RIkWMN9980y6gM4xr4a50rVdceHi43bTTp08bXl5ehnStV1J27Nq1y6hYsaJdDR4eHsbo0aPTfX9lJO1kuUKFCg61JycnG/7+/oa7u7vdtOrVqxuSsrWd7Lp+v278KVy4sPHpp586hOU3ExISYgQGBtq1vfvuu4Yko2PHjllax/VBzenTp209RiZMmOAw73333WdIMpYsWZLuurp3725IsgvWcvK+SQvObgz7DcMwVqxYkWFQ8+mnnxqhoaHGuHHjsrDn19wsqGnatKnDMklJSbbed+vXr3dYV8WKFY2kpCS7ZXbt2mWre/Xq1Q7rTOvhmpPeW4mJibag8/rgOScOHz5sSI494m4W1NSqVSvd12/a3/Krr75ymLZr1y7DYrEYvr6+Rnx8/C3VDQAAMsYYNfnshRdeUKFChfTFF1/owoULN50/MTFRixYt0ssvv6yhQ4dq8ODBGjx4sPbs2SNJOnz4cLrLdejQwaEtbYDSzKadO3fO1rZ9+3ZFRESoadOmKl68uMMy3t7eatCgga5cuWJXR0hIiA4cOJDueAAZWb9+vSSpf//+DtMCAwPTrfl66U2vWrWqJPt92rBhgySpY8eO6T4JI218l6z69ddfJUk9evRId/DYtDFrtmzZkq313grjukdz3+wxz1nVvXv3TKefOXNGX375pUaNGqVHHnlEgwcP1pNPPikPD48MX6PZlfYaGTBggMO0YsWKqUOHDrJarfrzzz8dpqf3+ggMDFRAQIDd6yPNjBkzdODAAfXs2TMXKs/Yxx9/rG3btum9997L8ngukvTBBx+oXbt2atmypXbv3q2YmBjt3r1bbdu21WuvvaZevXrZzd+hQwfVr19fMTEx6tSpk7Zs2aKYmBj99ddf6tSpk22MlewMgPzDDz+ocePGKlOmjDZv3qyYmBgdOnRIgwcP1gcffKC2bdumO25GZlq3bi0PDw+7Njc3N1WoUEHJycl243s1aNBAkvTQQw9p69atslqt2dpWdlz/eO7+/fsrLCxMsbGxevPNN7Vy5cp0l7l06ZKmTZum0aNH69FHH7V9dqftx+XLl23zrl69WpL0xBNPZKuuw4cPq1mzZjpw4ICmTJmicePG2U23Wq367bff5OPjo3vvvTfddbRo0UKS0v2Myur7Jjk5WZs2bZIk9evXz2GZjh07Zvi0wxEjRujAgQOaMGFCBnuZfekdR9zd3dW7d29J//ssuV7r1q0dntyVdlx0d3dX69atHZZJ77iZVdu2bdPVq1dVp04d3X333Vle7vDhw/r444/19NNP2z5r33rrLdu07OjatavDMeL6v2V6n7W1a9dW7dq1FRMTYzeeDQAAyF08njufBQcH66mnntLEiRP13//+Vx9++GGG86YN4vjvv/9mOE90dHS67SEhIQ5tvr6+N512/YlV2nZXrVp10xP+iIgIhYaGZjpPZtIG2CxXrly60zMaWDZN6dKlHdoKFy4syX6fbnU7N0r7Hb388st6+eWXM5wvPwdfjIiIsP07tx4FX7Zs2QynTZo0SePGjVNycnKubCsjaX+7jP5Gae1nzpxxmJbe60O69hq5/oQ5P504cUKvvfaaWrZs6TDYZ2bWrl2rMWPGqH79+po7d64tXKlVq5bmzZunhg0batmyZVqxYoU6deok6VpgN3/+fHXp0kXbtm2zOzEsXry4xo8fr1deeSXLr5fDhw9r0KBBKlasmJYuXWr7/KhSpYq++uornT17VkuXLtW3336rJ598Msv7ltnfSbJ/L7/zzjvas2ePlixZoiVLlsjf31/NmzdX9+7dNXDgQHl5eWV5uzdz4yDmkrRjxw61atVK3bt31969e+0+/3744QcNHTo00wGNo6OjFRAQIOnaIMKSsv2ks+HDhyslJUXvvvuuHn/8cYfpERERthpuDMDSm/dGWX3fXLp0SUlJSbbB2dNTrlw5XblyJdMacsvNPt/TG9A5s+NiiRIl5OrqmuH07AaSUvb/5oZhaMyYMfrwww/twvjrZfR9ICPpfa6n/S2DgoJUqFChdJcrX768du3ale5nLQAAyB0ENU7w/PPP6/PPP9eXX36psWPHpjuPYRjq27ev/v33Xw0bNkzDhg1TxYoV5evrK4vFopdeekkTJkzI8AtbZlfGs3rVPO0KdeXKlW1PhclIdnoD5IXsPgo5t6T9jpo3b26ax0nv2LFD0rWT5huvEGfkZr0RMjrp3bRpk0aPHi0/Pz99/PHHat26tUqUKGF7KlCpUqVydLU5JzILE531+sjMmjVrFBsbq4sXL9qehJUmLQCcOnWqVq9erbp16+qjjz6SdO1JLJLUs2dPh/1ydXVVr169tHPnTv3xxx+2oEa6dvK6c+dOLViwQBs3blR8fLzuuusuDRgwQPPnz5ck3XXXXVmqfc6cOUpOTlbHjh1tJ6vX69u3r5YuXao//vgjW0FNdv5OZcqU0bZt2/T7779r6dKlWrdunS20mThxov766688/VyqV6+ennjiCb3//vv64osvbH+fEydO2IK3jz76SF26dFFISIi8vb0lSU2bNtVff/2V4Wd3dvTr10+zZ8/Whx9+qO7du6tatWp209Pe176+vrr//vszXVd6f3szvm/ySm4cM/PSjz/+qEmTJqlMmTL68MMPFRYWpuDgYLm7uyspKUmenp7Zfk3lNMzMrZ6aAAAgYwQ1ThAUFKSnn35aEyZM0IQJE1SqVCmHedJuHWrYsKG++OILh+nHjh3L8zrTrqZWq1Yt3SvKualkyZI6ePCgTpw4oRo1ajhMz+jR4TnZTmbry+520n5HPXr00OjRo2+tuFzy448/SpLDyX/aFfWYmBiHk+u0q7vZtWDBAknSf/7zHw0aNMhuWnx8vM6fP5+j9aanVKlSOn78eIavkbRwI70r42aW2W2C//77r0OPutOnT0tSurfuXd+eXu8FNzc39enTR3369LFrT3vccnq3d6TnVmrITW5uburQoYPtFp0TJ07okUce0e+//653331XEydOzNPtV6hQQZL9LSfLly9XUlKSxowZo2eeecZhmfQ+u8uUKaMDBw7o6NGjqlWrVpa3/9hjj6lZs2YaPny42rZtq7Vr19pu+ZSuHWu8vLzk4uKiadOm5dkJdmBgoDw8PBQeHq74+HhbKHW99B4Dnldu9vme3jE3v5UpU0aSdPTo0SzNn/ZZ+8UXX6hLly5203Lz+0Da3zIiIkKxsbHp9qq5XT9rAQC4nTj/MlEBNXr0aBUuXFhTpkxJt/tw2glOel3Pr1y5olWrVuV5jY0aNZKfn5/WrVuX57eHpI2T8NNPPzlMu3z5sm0smFvVvHlzSdLKlSsVFRXlMH3OnDnZWl/79u0l/e9LtLOtXbtWc+bMkcVi0dNPP203LS2kOnTokMNyOX09ZfY6nTt3brpXeNMCo7RxUbIq7TXyww8/OEwLDw/XL7/8IovFctPeX2YxePBgGdcGdHf4ef311yVJb731lgzD0Nq1a23LlShRQtK1MS7Ss3XrVklZv43v/PnzmjdvngIDAx3GtslIbteQW8qVK6cXXnhBkrR37948317aCfL1wWdm74k//vgj3bHJ2rVrJ0maMmVKtmt48skn9dlnn+ncuXNq06aNjhw5Ypvm5uam1q1bKyoqSr/99lu2151V7u7uttvp0vsM//XXX/P1FsP0akhJSdHPP/8s6X/HAWdq0KCBihYtql27dmVpDLPMXlfp7W9Oubu7q0mTJpLSPx7u3btXu3btkq+vr+rWrZtr2wUAAPYIapwkMDBQI0eOVGJioqZOneowvXLlynJxcdHvv/9ud7U2ISFBw4YNy5cvvZ6enho7dqyio6PVq1evdK/anTlzxnYrxvVt1apVc+iGn5khQ4bI09NTs2bNsg2sKV0b2PDZZ59VbGxsznfkOpUqVVKHDh0UFRWl0aNHKzU11TZt+fLlmjt3brbWd/fdd6t9+/b6888/9dRTT6Ub/uzatcthwNHp06fLYrFkuQfDzSQkJOizzz5Tly5dlJqaqldffVU1a9a0m6dVq1aSpAkTJtjt9w8//JBu+JEVaVfvp06dajdGzb59+2wnzDdKu5p98ODBbG3rqaeekouLiz755BO7gCApKUlPP/204uPj1atXL9uV6lvx8MMPq1q1aqYJ4K7Xo0cPSdKsWbO0dOlSu2mLFi3S7Nmz5eLi4jAQ8t69ex3GSjp9+rTuu+8+RUdH64MPPnDoCbFgwQJVq1ZNDz/8sF37fffdJ+la8HBjj79NmzbZxt5KG7w1L3z44Yfp9thavny5JDm8Dlq3bi2LxZJrvQN37NhhC1Y6d+5sa097T8ycOdPuc+vMmTMaNmxYuut67LHHFBQUpBUrVuijjz5yCDg3bdqkixcvZljLU089pY8//lhnz55VmzZt7HppvPzyy3JxcdGQIUPsAr80MTEx+vbbbxUfH3/znc5E2i1ur7/+ul3vmYiICD3//PMZLvfZZ5+pWrVqevHFF29p+9fbsGGDvv32W7u2tLpq165tC32dydPTU88++6wk6dFHH3XoBRQZGal169bZ/p/2upoyZYrd62P9+vV67733crW2tJB//Pjxdsf96OhojRgxQoZh6IknnsjVcaAAAIA9bn1yotGjR+vTTz9N9+S+WLFievTRR/X111+rTp06atu2rby9vbV+/XqlpqZq8ODBeX47kiSNGzdOBw4c0Pfff6/q1aurXr16qlChgpKSknTw4EHt27dPtWvX1kMPPWRbJjk5Odsn4RUqVNAHH3ygESNG6N5771XLli1VokQJbdq0SVeuXNGAAQM0a9asXNmnL774Qk2bNtU333yjNWvWqFGjRjp79qzWr1+v4cOHa/Lkydla38yZM9WxY0d9/vnnmj17turWratSpUopMjJSu3fv1qlTp/TMM8+oY8eOtmXSxo7I6hgy1/vmm29sJ1xxcXE6f/68/v77b8XFxcnT01MTJ07UmDFjHJZ76qmn9OWXX2revHmqUaOGateurcOHD2vv3r165plnMh3YOiNDhgzRBx98oCVLlig0NFSNGjXS5cuXtW7dOvXo0UNbtmxxOAEpX768ateurW3btqlx48a666675Orqqu7du2f6dKnGjRvrrbfe0ssvv6ywsDC1bt1aQUFB+vPPP3Xq1ClVqVIl23+7jJw8eVIHDx5UZGRktpZLuxItScePH5d0rVfMl19+KUmqX7++Pv/881uqrUePHurTp4/mzp2rbt26qWHDhqpQoYKOHz9uC7D+85//OAzu/f7772vBggWqX7++SpYsqYsXL2rDhg1KTEzUq6++6nDrmnTtZPHgwYO2HjRp6tevrzFjxuj999+3vWdq1Kihs2fP6q+//pLVatXQoUNtPUXywhtvvKExY8aoTp06qlKligzD0K5du3To0CEFBAQ4vAdu5T13/WDPSUlJOnHihDZt2iSr1apu3brZff51795dd911l7Zt22Yb3yshIUFr1qxR3bp11bRpU9utZmkCAgI0d+5cde/eXc8++6w++eQTNWrUSPHx8dq/f7+OHDmiHTt2qFixYhnWOHLkSFmtVj377LNq06aN1q1bpwoVKqh58+aaPHmyRowYoTZt2qhmzZqqWrWq3N3d9e+//2rnzp1KTExUr1690r1lKaseeOABLViwQHPnzlWNGjV0zz33yM3NTb///rsqVqyoJk2a2J4mdL2IiAgdPHgwV8eyevLJJ/XYY4/pq6++UqVKlbR79279888/KlKkSL4cN7PqpZde0o4dO7Rw4UJVrVpVLVq0ULFixXTq1Clt375d7du3twXsI0eO1PTp0/X5559r7dq1ql27ts6cOaMNGzZo9OjRev/993Otrt69e2vo0KGaMmWKatasqbZt28rHx0dr165VeHi4mjRpojfffDPXtgcAABzRo8aJ/P39NWrUqAynf/HFF/rggw9UoUIF/fbbb1q/fr3atWunbdu2ZfhUi9zm4uKiGTNmaNGiRWrfvr2OHz+un3/+WRs2bJCXl5eef/55hyuXOfXUU09pwYIFatSokTZv3qxffvlFderU0aZNm1S5cuVc2YZ07ZGqmzdv1oMPPqirV69q4cKFioqK0rRp09INOG6mWLFi2rhxoz755BPVqFFDO3bs0Lx587R7925VrFhR7733nsN6d+3aJUkOPRWy4s8//9R3332nGTNmaOXKlTp58qTatGmjiRMn6uTJk3r++efTHYuiePHi+uOPP9S1a1edO3dOK1askJ+fn1atWnXTx29nJDAwUFu3btWDDz6opKQkLV68WGfOnNFbb72VaS+dn3/+WT169NCxY8c0Y8YMTZ06Vdu3b7/p9l566SUtXbpUrVq10tatWzV//nxbz6/Nmzen+xj5/LR582bbT1oPiGPHjtna9u3bd8vbsFgs+vHHHzV16lS1bNlSR44c0YIFC/Tvv/+qc+fOWrFihV566SWH5Xr06KGwsDAdOHBA8+bN0549e9SxY0etWbMmRydd7733nubPn68OHTro/PnzWrBggfbt26dWrVpp9uzZ+uqrr255XzPz6aefqn///oqLi9OKFSu0cuVKubm56bnnntPu3btVpUoV27xWq1V79+6Vv7+/unbtmu1tfffdd7afH3/8UQcOHFDLli01depULVy40G6wWQ8PD61fv15PPvmkvLy8tHTpUu3fv19PP/20Vq1alWFQ1Lp1a+3atUvDhg2TYRhauHCh/vzzT/n5+enNN9/M0mDlo0aN0vvvv69Tp06pTZs2trFEhg0bpm3btmnQoEGKjo7W0qVL9csvvygmJkYDBgzQ0qVLMxxvKDtmz56td999VyEhIVq5cqU2bdqkBx98UL///rttgPH80LdvXy1evFiurq5atGiRrefYX3/9pXr16uVbHTfj5uamn3/+WdOnT1eTJk20bds2zZ8/X6dPn1bXrl3tvh9UrVpV27ZtU7du3RQREaHFixcrJiZGX331Va73qJGkr776SjNmzFC9evVsA3UXK1ZM//nPf/T7779n+HQvAACQOyxGbjx6Ashj48eP1xtvvKFp06Zl61HGZlW7dm0lJSXpn3/+SfexrwByz/bt29WgQQO9/fbbevnll51dDgAAAJApbn0C8llERIT27t2rWbNmEdIA+WDNmjW2ccEAAAAAsyOoAfJZUFCQbbwMAHlv9OjRGj16tLPLAAAAALKEoCYPWK1WnT17VoULF053rBBkX2JioiQpPj4+3cGXAQBwlkOHDmV5QPYmTZqkO3g3AABwDsMwFB0drVKlStmNPehMjFGTB06fPp0rjwjOCwEBAfL19ZWLi4uSkpJ05cqVmz6W1cfHR76+vvL09JSrq6tSUlIUFxenq1evOvQMsVgs8vPzk6+vr9zc3GS1WpWQkKArV67YPb4ZAAAAAACzOHXqlEqXLu3sMiTRoyZPFC5cWJK0/8BB27/N4I3xr2vt2rXq06evSpcprRUrVujA/v36+JNPVLt2nQyX69a1iwIDg9SiRQsVL15cR48d1eJFi1S7Th1Nnfqt3dM8XnnlZf25YYO6deumqlVDFRERoQUL5qtYsWKa/t0Mh8f8AgAAAADgLNHR0apeLdRU5+4ENXkg7XanwoULq0iRIk6u5po9e/bot99+07PPPadBgwZLkvr06ave9/fSlClTNGPG9xku+/4Hk9SoUSO7trp16+rVV17R+vV/qFev+yVJFy5c0B/r1unhQYP03HP/Gw8iLCxMjz/+mDZv3qyHHnoo93cOAAAAAIBbYKZhS8xxAxby3OrVq+Tq6qr77+9ta/P09FSPnj21e9cunT9/PsNlbwxpJKlt23skScePHbe1xcXFSZICAwPt5g0KDpYkeV3X8wYAAAAAADiiR00BceDAAZUrV06+vr527TVr1pQkHTxwIFu3JUVEREiSivoXtbWVLl1axYsX1/czZqh8ufKqVr2aLl4M10cffaiQkBDd27Hjre8IAAAAAAB3MIKaAiIiPFxBQUEO7UFB13q7hIeHZ2t906d9K1dXV7Vr197W5u7urvc/mKSXXhynZ54ZaWuvUaOGvpvxvWluAwMAAAAAwKwIagqIxMREuXt4OLSnDQSckJiQ5XUtX75MCxYs0ODBQ1SuXDm7aUWKFFFoaKjat++gWrVr69TJk/r226l6fsxoffnVFLuBhwEAAAAAgD2CmgLC09NTyUlJDu2JiYmSJC9PryytZ/v2v/XG+PFq2rSpRjz9tN206OhoPTJksAYNGqyHBw2ytde46y499ugjWrRoofr27XcLewEAAAAAwJ2NwYQLiKDgYNu4MteLiLh2y1Pw/w/4m5mDBw/qmZEjValyZb3/wSS5udnnfL+tXq1Lly6pVevWdu0NGzaUr6+vdu7YmeP6AQAAAAAoCAhqCojQ0FCdOHFCMTExdu179uy5Nr1atUyXP3XqlJ4a/qQCAgL02WeT5ePj4zDPpcuXJElWq9Wu3TAMpaamKjU19VZ2AQAAAACAOx5BTQHRvl17paam6uef59nakpKStGjRItWqVcv2xKdz587p+PHjdstGREToyWFPyGJx0edffKmAgIB0t5E2Xs3KlSvs2teuXav4+HhVu0kYBAAAAABAQccYNQVErdq11b5DB336ySe6fPmyypQpqyVLFuvc2bMaP368bb5XXnlZf2/bpp27dtvahg9/UqdPn9bgwUO0c8cO7dyxwzYtIDBQYWFhkqRWrVqrUqVKmvLVVzp39pxq166tk6dO6sc5cxQcHKwePXvm2/4CAAAAAHA7IqgpQN5++z+aPPkzLVu6VFFRUapSpao++eRTNWjQMNPlDh08KEmaPn2aw7QGDRvaghp3d3dNm/6dpnz1ldav/0MrV65QoUKF1KZNGz399Ej5+/vn/k4BAAAAAHAHsRiGYTi7iDtNVFSU/Pz8dPrMWRUpUsTZ5QAAAAAAgHRERUWpdEgpRUZGmub8nTFqAAAAAAAATIKgBgAAAAAAwCQIagAAAAAAAEyCoAYAAAAAAMAkCGoAAAAAAABMgqAGAAAAAADAJAhqAAAAAAAATIKgBgAAAAAAwCQIagAAAAAAAEyCoAYAAAAAAMAkCGoAAAAAAABMws3ZBcC8Pv30E50+dTrdaRaL9PzYFxQYGJjPVQEAAAAAcOciqEGGQkJCNPWbb9KddneTJoQ0AAAAAAD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",
      "text/plain": [
       "<Figure size 1300x535.935 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Constructing a GE Experiment and a QND GE Experiment\n",
    "\n",
    "ge_exp, qnd_exp = integrated_fidelity_experiment(qubit=qubit_0, backend=backend)\n",
    "\n",
    "print(len(ge_exp))\n",
    "print(len(qnd_exp))\n",
    "\n",
    "qnd_exp[1].draw(\n",
    "    backend=backend,\n",
    "    style=stylesheet.IQXDebugging(),\n",
    "    show_framechange=True,\n",
    "    show_waveform_info=True,\n",
    "    show_barrier=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101\n",
      "101\n",
      "{'Total Experiment Size': 202, 'Frequency Step Size (MHz)': 0.01, 'Frequency Span (MHz)': 1.0}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x535.935 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resonator Frequency Sweep Experiment\n",
    "\n",
    "rr_freq_exp_g, rr_freq_exp_e, details = rr_freq_spec(\n",
    "    qubit=qubit_0,\n",
    "    backend=backend,\n",
    "    freq_span=1.0 * MHz,\n",
    "    num_experiments=101\n",
    ")\n",
    "\n",
    "print(len(rr_freq_exp_g))\n",
    "print(len(rr_freq_exp_e))\n",
    "print(details)\n",
    "\n",
    "rr_freq_exp_e[-1].draw(\n",
    "    backend=backend,\n",
    "    style=stylesheet.IQXDebugging(),\n",
    "    show_framechange=True,\n",
    "    show_waveform_info=True,\n",
    "    show_barrier=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constructing Qubit T1 Decay Experiment\n",
    "\n",
    "from typing import Optional\n",
    "\n",
    "def qubit_t1_exp(qubit: int, backend, min_delay: Optional[float] = 0.0, max_delay: Optional[float] = 100.0 * us, num_exp: Optional[int] = 101):\n",
    "    delay_linspace = np.linspace(min_delay, max_delay, num_exp)\n",
    "\n",
    "    t1_decay_exp = []\n",
    "\n",
    "    for t1_delay in delay_linspace:\n",
    "        t1_delay_dt = get_closest_multiple_of_16(get_dt_from(t1_delay))\n",
    "        with pulse.build(backend=backend, default_alignment='sequential', name=f'Qubit T1, Delay: {int(t1_delay_dt * dt/us * 1e3) / 1e3}us') as t1_sched:\n",
    "            qubit_chan = pulse.drive_channel(qubit)\n",
    "\n",
    "            pulse.play(x_pulse, qubit_chan)\n",
    "            pulse.delay(t1_delay_dt, qubit_chan)\n",
    "            pulse.measure(qubit, pulse.RegisterSlot(qubit))\n",
    "        t1_decay_exp.append(t1_sched)\n",
    "\n",
    "    return t1_decay_exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Total Experiment Size': 101, 'Frequency Step Size (us)': 1.0, 'Frequency Span (us)': 100.0}\n",
      "101\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x535.935 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making T1 Decay Experiment\n",
    "\n",
    "from helper_funcs.helpers import qubit_t1_exp\n",
    "\n",
    "t1_decay_exp, t1_details = qubit_t1_exp(\n",
    "    qubit=qubit_0,\n",
    "    backend=backend,\n",
    "    min_delay=0.0 * us,\n",
    "    max_delay=100.0 * us,\n",
    "    num_exp=101\n",
    ")\n",
    "\n",
    "print(t1_details)\n",
    "\n",
    "print(len(t1_decay_exp))\n",
    "t1_decay_exp[-1].draw(\n",
    "    backend=backend,\n",
    "    style=stylesheet.IQXDebugging(),\n",
    "    show_framechange=True,\n",
    "    show_waveform_info=True,\n",
    "    show_barrier=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "hooke_env",
   "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": "3.9.11"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}