{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a706ab82-02f1-41ab-9400-1b0d3d887547",
   "metadata": {},
   "source": [
    "# Compare transpiler settings\n",
    "\n",
    "## Background\n",
    "\n",
    "To ensure faster and more efficient results, as of 1 March 2024, circuits and observables need to be transformed to only use instructions supported by the system (referred to as instruction set architecture (ISA) circuits and observables) before being submitted to the Qiskit Runtime primitives.  One common way to do this is to use the transpiler's `generate_preset_pass_manager` function.  However, you might choose to follow a more manual process.\n",
    "\n",
    "For example, you might want to target a specific subset of qubits on a specific device. This tutorial tests the performance of different transpiler settings by completing the full process of creating, transpiling, and submitting circuits."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dd2e666-4d9b-4932-983c-a4468c4a04c9",
   "metadata": {},
   "source": [
    "## Requirements\n",
    "\n",
    "Before starting this tutorial, ensure that you have the following installed:\n",
    "\n",
    "* Qiskit SDK 1.0 or later, with visualization support (`pip install 'qiskit[visualization]'`)\n",
    "* Qiskit Runtime (`pip install qiskit-ibm-runtime`) 0.22 or later"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16849d1b-cb78-4106-abaf-c7b113fb2e94",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4a9547b3-a09a-4563-b027-12fa7350d5e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create circuit to test transpiler on\n",
    "from qiskit import QuantumCircuit, transpile\n",
    "from qiskit.circuit.library import GroverOperator, Diagonal\n",
    "\n",
    "# Use Statevector object to calculate the ideal output\n",
    "from qiskit.quantum_info import Statevector\n",
    "from qiskit.visualization import plot_histogram\n",
    "\n",
    "# Qiskit Runtime\n",
    "from qiskit_ibm_runtime import QiskitRuntimeService, Batch, SamplerV2 as Sampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1f7d428d-c197-405c-a52c-c9a614cce00c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ibm_wellington'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To run on hardware, select the backend with the fewest number of jobs in the queue\n",
    "service = QiskitRuntimeService(channel=\"ibm_quantum\")\n",
    "backend = service.least_busy(operational=True, simulator=False)\n",
    "backend.name"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "853701d3-dcd0-4b40-8c4a-e3bb619b2af4",
   "metadata": {},
   "source": [
    "## Step 1: Map classical inputs to a quantum problem\n",
    "\n",
    "Create a small circuit for the transpiler to try to optimize. This example creates a circuit that carries out Grover's algorithm with an oracle that marks the state `111`. Next, simulate the ideal distribution (what you'd expect to measure if you ran this on a perfect quantum computer, an infinite number of times) for comparison later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "20f1ab33-5bca-450f-8ae1-b9b1aae10fe0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 287.496x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oracle = Diagonal([1] * 7 + [-1])\n",
    "qc = QuantumCircuit(3)\n",
    "qc.h([0, 1, 2])\n",
    "qc = qc.compose(GroverOperator(oracle))\n",
    "\n",
    "qc.draw(output=\"mpl\", style=\"iqp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b1913f32-7234-441e-8a60-69cf24073500",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ideal_distribution = Statevector.from_instruction(qc).probabilities_dict()\n",
    "\n",
    "plot_histogram(ideal_distribution)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41485f5d-38f0-4e50-ae31-2c65abd63544",
   "metadata": {},
   "source": [
    "## Step 2: Optimize problem for quantum execution\n",
    "\n",
    "Next, transpile the circuits for the backend. You will compare the performance of the transpiler with `optimization_level` set to `0` (lowest) against `3` (highest). The lowest optimization level just does the bare minimum needed to get the circuit running on the device; it maps the circuit qubits to the device qubits, and adds swaps gates to allow all 2-qubit operations. The highest optimization level is much smarter and uses lots of tricks to reduce the overall gate count. Since multi-qubit gates have high error rates, and qubits decohere over time, the shorter circuits should give better results.\n",
    "\n",
    "The following cell transpiles `qc` for both values of `optimization_level`, prints the number of CNOT gates, and adds the transpiled circuits to a list. Some of the transpiler's algorithms are randomized, so it sets a seed for reproducibility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "237db5b2-d285-4974-8377-b4a902fa2208",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNOTs (optimization_level=0):  27\n",
      "CNOTs (optimization_level=3):  14\n"
     ]
    }
   ],
   "source": [
    "# Need to add measurements to the circuit\n",
    "qc.measure_all()\n",
    "\n",
    "circuits = []\n",
    "for optimization_level in [0, 3]:\n",
    "    t_qc = transpile(qc, backend, optimization_level=optimization_level, seed_transpiler=0)\n",
    "    print(f\"CNOTs (optimization_level={optimization_level}): \", t_qc.count_ops()[\"cx\"])\n",
    "    circuits.append(t_qc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5c984f0-5b5a-4c13-8b67-17d8f4f3c491",
   "metadata": {},
   "source": [
    "Since CNOTs usually have a high error rate, the circuit transpiled with `optimization_level=3` should perform much better.\n",
    "\n",
    "Another way you can improve performance is through [dynamic decoupling](https://docs.quantum-computing.ibm.com/api/qiskit-ibm-provider/qiskit_ibm_provider.transpiler.passes.scheduling.PadDynamicalDecoupling#paddynamicaldecoupling), by applying a sequence of gates to idling qubits. This cancels out some unwanted interactions with the environment. The following cell adds dynamic decoupling to the circuit transpiled with `optimization_level=3` and adds it to the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5907249d-3de4-40c0-bf18-16267e35fd0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.transpiler import PassManager\n",
    "from qiskit_ibm_runtime.transpiler.passes.scheduling import (\n",
    "    ASAPScheduleAnalysis,\n",
    "    PadDynamicalDecoupling,\n",
    ")\n",
    "from qiskit.circuit.library import XGate\n",
    "\n",
    "# Get gate durations so the transpiler knows how long each operation takes\n",
    "durations = backend.target.durations()\n",
    "\n",
    "# This is the sequence we'll apply to idling qubits\n",
    "dd_sequence = [XGate(), XGate()]\n",
    "\n",
    "# Run scheduling and dynamic decoupling passes on circuit\n",
    "pm = PassManager([ASAPScheduleAnalysis(durations), PadDynamicalDecoupling(durations, dd_sequence)])\n",
    "circ_dd = pm.run(circuits[1])\n",
    "\n",
    "# Add this new circuit to our list\n",
    "circuits.append(circ_dd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bbccc8cc-3713-487e-a104-e251062c7d02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2252.61x1204 with 1 Axes>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circ_dd.draw(output=\"mpl\", style=\"iqp\", idle_wires=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "121ca6f2-f017-4ea0-b82c-80c9f6823b93",
   "metadata": {},
   "source": [
    "## Step 3: Execute using Qiskit Primitives\n",
    "\n",
    "At this point, you have a list of circuits transpiled for the specified system. Next, create an instance of the sampler primitive and start a batched job using the context manager (`with ...:`), which automatically opens and closes the batch.\n",
    "\n",
    "Within the context manager, sample the circuits and store the results to `result`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a682fe13-e365-4321-9461-8187e0e7ac17",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "float() argument must be a string or a real number, not 'complex'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[22], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m Batch(service\u001b[38;5;241m=\u001b[39mservice, backend\u001b[38;5;241m=\u001b[39mbackend):\n\u001b[0;32m      2\u001b[0m     sampler \u001b[38;5;241m=\u001b[39m Sampler()\n\u001b[1;32m----> 3\u001b[0m     job \u001b[38;5;241m=\u001b[39m \u001b[43msampler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcircuits\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      4\u001b[0m     result \u001b[38;5;241m=\u001b[39m job\u001b[38;5;241m.\u001b[39mresult()\n",
      "File \u001b[1;32mC:\\myenv\\myenv\\Lib\\site-packages\\qiskit_ibm_runtime\\sampler.py:112\u001b[0m, in \u001b[0;36mSamplerV2.run\u001b[1;34m(self, pubs, shots)\u001b[0m\n\u001b[0;32m     94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun\u001b[39m(\u001b[38;5;28mself\u001b[39m, pubs: Iterable[SamplerPubLike], \u001b[38;5;241m*\u001b[39m, shots: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m RuntimeJobV2:\n\u001b[0;32m     95\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Submit a request to the sampler primitive.\u001b[39;00m\n\u001b[0;32m     96\u001b[0m \n\u001b[0;32m     97\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    110\u001b[0m \u001b[38;5;124;03m        ValueError: Invalid arguments are given.\u001b[39;00m\n\u001b[0;32m    111\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 112\u001b[0m     coerced_pubs \u001b[38;5;241m=\u001b[39m [\u001b[43mSamplerPub\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcoerce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpub\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshots\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m pub \u001b[38;5;129;01min\u001b[39;00m pubs]\n\u001b[0;32m    114\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28mlen\u001b[39m(pub\u001b[38;5;241m.\u001b[39mcircuit\u001b[38;5;241m.\u001b[39mcregs) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m pub \u001b[38;5;129;01min\u001b[39;00m coerced_pubs):\n\u001b[0;32m    115\u001b[0m         warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    116\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOne of your circuits has no output classical registers and so the result \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    117\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwill be empty. Did you mean to add measurement instructions?\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    118\u001b[0m             \u001b[38;5;167;01mUserWarning\u001b[39;00m,\n\u001b[0;32m    119\u001b[0m         )\n",
      "File \u001b[1;32mC:\\myenv\\myenv\\Lib\\site-packages\\qiskit\\primitives\\containers\\sampler_pub.py:126\u001b[0m, in \u001b[0;36mSamplerPub.coerce\u001b[1;34m(cls, pub, shots)\u001b[0m\n\u001b[0;32m    124\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(values, (BindingsArray, Mapping)):\n\u001b[0;32m    125\u001b[0m         values \u001b[38;5;241m=\u001b[39m {\u001b[38;5;28mtuple\u001b[39m(circuit\u001b[38;5;241m.\u001b[39mparameters): values}\n\u001b[1;32m--> 126\u001b[0m     parameter_values \u001b[38;5;241m=\u001b[39m \u001b[43mBindingsArray\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcoerce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    127\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    128\u001b[0m     parameter_values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[1;32mC:\\myenv\\myenv\\Lib\\site-packages\\qiskit\\primitives\\containers\\bindings_array.py:300\u001b[0m, in \u001b[0;36mBindingsArray.coerce\u001b[1;34m(cls, bindings_array)\u001b[0m\n\u001b[0;32m    298\u001b[0m     bindings_array \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m()\n\u001b[0;32m    299\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(bindings_array, _Mapping):\n\u001b[1;32m--> 300\u001b[0m     bindings_array \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbindings_array\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    301\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(bindings_array, BindingsArray):\n\u001b[0;32m    302\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m bindings_array\n",
      "File \u001b[1;32mC:\\myenv\\myenv\\Lib\\site-packages\\qiskit\\primitives\\containers\\bindings_array.py:123\u001b[0m, in \u001b[0;36mBindingsArray.__init__\u001b[1;34m(self, data, shape)\u001b[0m\n\u001b[0;32m    118\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m    119\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    120\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m    121\u001b[0m         (\n\u001b[0;32m    122\u001b[0m             _format_key((key,)) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, (Parameter, \u001b[38;5;28mstr\u001b[39m)) \u001b[38;5;28;01melse\u001b[39;00m _format_key(key)\n\u001b[1;32m--> 123\u001b[0m         ): \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mval\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    124\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m key, val \u001b[38;5;129;01min\u001b[39;00m data\u001b[38;5;241m.\u001b[39mitems()\n\u001b[0;32m    125\u001b[0m     }\n\u001b[0;32m    127\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shape \u001b[38;5;241m=\u001b[39m _infer_shape(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data) \u001b[38;5;28;01mif\u001b[39;00m shape \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m shape_tuple(shape)\n\u001b[0;32m    128\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_parameters \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[1;32mC:\\myenv\\myenv\\Lib\\site-packages\\qiskit\\circuit\\library\\standard_gates\\rz.py:138\u001b[0m, in \u001b[0;36mRZGate.__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m    135\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m    137\u001b[0m ilam2 \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.5\u001b[39mj \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mfloat\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparams[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m--> 138\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[43mexp\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43milam2\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexp\u001b[49m\u001b[43m(\u001b[49m\u001b[43milam2\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mTypeError\u001b[0m: float() argument must be a string or a real number, not 'complex'"
     ]
    }
   ],
   "source": [
    "with Batch(service=service, backend=backend):\n",
    "    sampler = Sampler()\n",
    "    job = sampler.run(\n",
    "        [circuits],  # sample all three circuits\n",
    "        shots=8000\n",
    "    )\n",
    "    result = job.result()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "006d6e89-db7c-4713-b42c-5b5f743d4b2e",
   "metadata": {},
   "source": [
    "## Step 4: Post-process, return result in classical format\n",
    "\n",
    "Finally, plot the results from the device runs against the ideal distribution. You can see the results with `optimization_level=3` are closer to the ideal distribution due to the lower gate count, and `optimization_level=3 + dd` is even closer due to the dynamic decoupling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45828f96-4523-4a8b-9a45-ae03b4769bf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "binary_prob = [{k: v / res.data.meas.num_shots for k, v in res.data.meas.get_counts().items()} for res in result]\n",
    "plot_histogram(\n",
    "    binary_prob + [ideal_distribution],\n",
    "    bar_labels=False,\n",
    "    legend=[\n",
    "        \"optimization_level=0\",\n",
    "        \"optimization_level=1\",\n",
    "        \"optimization_level=1 + dd\",\n",
    "        \"ideal distribution\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5182020c-9912-4710-b4c0-83652222507d",
   "metadata": {},
   "source": [
    "You can confirm this by computing the [Hellinger fidelity](https://docs.quantum-computing.ibm.com/api/qiskit/quantum_info) between each set of results and the ideal distribution (higher is better, and 1 is perfect fidelity)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "719f98a3-c31a-42aa-9590-ba58a2389171",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.quantum_info import hellinger_fidelity\n",
    "\n",
    "for prob in binary_prob:\n",
    "    print(f\"{hellinger_fidelity(prob, ideal_distribution):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b90d5e07-c36a-48a5-823c-96638a4d001a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import qiskit_ibm_runtime\n",
    "\n",
    "qiskit_ibm_runtime.version.get_version_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "274379ca-c102-47b9-b906-b18fa2a80bcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import qiskit\n",
    "\n",
    "qiskit.version.get_version_info()"
   ]
  }
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