Codebeispiele-Linux-Magazin.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f2a203e8",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# How to use this notebook\n",
    "This notebook is meant to accompany an article published in \"Linux Magazin\" XXXXX (german). The code snippets directly refer to the article are probably not self-explanatory."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a39bffc",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### _Vorbereitungen_\n",
    "Imports der benötigten Bibliotheken, API einrichten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e11ca67e",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from qiskit import QuantumCircuit, execute,  Aer, QuantumRegister, IBMQ\n",
    "from qiskit.visualization import *\n",
    "from qiskit.circuit.library import QFT\n",
    "from qiskit_ibm_runtime import QiskitRuntimeService, Sampler\n",
    "from qiskit_aqt_provider import AQTProvider\n",
    "from qiskit.visualization import plot_histogram\n",
    "from qiskit.compiler import transpile, assemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "211032ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.providers.ibmq import least_busy\n",
    "\n",
    "IBMQ_API_TOKEN = \"PUT_YOUR_IBMQ_TOKEN_HERE\"\n",
    "AQT_TOKEN = \"PUT_YOUR_AQT_TOKEN_HERE\"\n",
    "IBMQ.enable_account(IBMQ_API_TOKEN)\n",
    "provider = IBMQ.get_provider('ibm-q')\n",
    "device = provider.get_backend('ibmq_qasm_simulator')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "078b9d80",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 4. Erste Schritte / Praxisbeispiele"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "67b81c42",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'000': 1968, '111': 2032}\n"
     ]
    }
   ],
   "source": [
    "simulator = Aer.get_backend('qasm_simulator')\n",
    "\n",
    "qc1 = QuantumCircuit(3)\n",
    "qc1.h(0)\n",
    "qc1.cx(0, 1)\n",
    "qc1.cx(0, 2)\n",
    "qc1.measure_all()\n",
    "\n",
    "# Schaltkreis mit Aer simulieren\n",
    "job = execute(qc1, simulator, shots=4000)\n",
    "result = job.result()\n",
    "counts=result.get_counts()\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "479cace5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'000': 2040, '111': 1960}\n"
     ]
    }
   ],
   "source": [
    "# Schaltkreis auf Quantencomputer laufen lassen\n",
    "\n",
    "# Im Normalfall wird dieses Snippet durch einen Simulator berechnet, um lange Wartezeiten zu vermeiden. Um es dennoch auf einem echten Gerät laufen zu lassen, entfernen Sie den Hashtag # vor Zeile 5.\n",
    "\n",
    "# device = least_busy(provider.backends(filters=lambda x: x.configuration().n_qubits >= 3 and not x.configuration().simulator and x.status().operational==True))\n",
    "\n",
    "job = execute(qc1, backend = device, shots=4000)\n",
    "result = job.result()\n",
    "counts=result.get_counts()\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9d8232e",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Lokales Beispiel mit Qiskit Runtime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d5047157",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'quasi_dists': [{'10011': 0.00390625, '01110': 0.0029296875, '01100': 0.005859375, '01010': 0.0146484375, '01000': 0.033203125, '11010': 0.052734375, '00010': 0.05859375, '01111': 0.0029296875, '10101': 0.0146484375, '00110': 0.0498046875, '00111': 0.0341796875, '00100': 0.0546875, '00011': 0.05859375, '10010': 0.001953125, '11111': 0.060546875, '10110': 0.0205078125, '11110': 0.060546875, '11100': 0.0693359375, '00000': 0.048828125, '01011': 0.0166015625, '01101': 0.005859375, '00001': 0.0537109375, '11001': 0.04296875, '11011': 0.044921875, '10100': 0.0166015625, '11101': 0.0556640625, '11000': 0.02734375, '10111': 0.0283203125, '00101': 0.037109375, '01001': 0.0224609375}], 'metadata': [{'header_metadata': {'circuit_digest': '9b6ce8fe003c09467ca2f2f1673c77aed4de1ffd043ebd71d3aea51fbb493bb3'}, 'shots': 1024}]}\n"
     ]
    }
   ],
   "source": [
    "N = 5\n",
    "qc2 = QuantumCircuit(N)\n",
    "qc2.h(0)\n",
    "for x in range(1, N):\n",
    "  qc2.cx(0,x)\n",
    "#qc2 += QFT(num_qubits=N)\n",
    "qc2.compose(QFT(num_qubits=N), inplace=True)\n",
    "qc2.measure_all()\n",
    "program_inputs = {\n",
    "    \"circuits\": qc2,\n",
    "    \"circuit_indices\": [0],\n",
    "}\n",
    "service = QiskitRuntimeService(channel=\"ibm_quantum\", token=IBMQ_API_TOKEN)\n",
    "options = {'backend_name': \"ibmq_qasm_simulator\"}\n",
    "job = service.run(\n",
    "    program_id=\"sampler\",\n",
    "    options=options,\n",
    "    inputs=program_inputs,\n",
    ")\n",
    "\n",
    "#Dieser Teil ist blockierend. Man wartet auf das Ergebnis.\n",
    "print(job.result())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cfca122",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### AQT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7f629206",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<AQTSimulator('aqt_qasm_simulator')>, <AQTSimulatorNoise1('aqt_qasm_simulator_noise_1')>, <AQTDevice('aqt_innsbruck')>]\n"
     ]
    }
   ],
   "source": [
    "aqt = AQTProvider(AQT_TOKEN)\n",
    "print(aqt.backends())\n",
    "sim_backend = aqt.backends.aqt_qasm_simulator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a1485e0",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Error Mitigation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e89423b8",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Schaltkreis mit Aer simulieren\n",
    "\n",
    "counts = execute(qc1, simulator, shots=10000).result().get_counts()\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "98f2da0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'000': 4982, '001': 125, '010': 101, '011': 212, '100': 64, '101': 233, '110': 162, '111': 4121}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Schaltkreis auf Quantencomputer laufen lassen\n",
    "\n",
    "device = least_busy(provider.backends(filters=lambda x: x.configuration().n_qubits >= 3 and not x.configuration().simulator and x.status().operational==True))\n",
    "results = execute(qc1, device, shots=10000).result()\n",
    "noisy_counts = results.get_counts()\n",
    "print(noisy_counts)\n",
    "\n",
    "# Beispielhaftes Ergebnis: {'000': 4973, '001': 70, '010': 145, '011': 334, '100': 55, '101': 254, '110': 173, '111': 3996}\n",
    "\n",
    "plot_histogram(noisy_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3116116a",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Deprecated Library was updated for this notebook to work\n",
    "#from qiskit.ignis.mitigation.measurement import complete_meas_cal, CompleteMeasFitter\n",
    "from qiskit.utils.mitigation import complete_meas_cal,CompleteMeasFitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a19ec2a1-197d-49f6-9dd9-7cfc206b2122",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Circuit mcalcal_000\n",
      "        ░ ┌─┐      \n",
      "q33_0: ─░─┤M├──────\n",
      "        ░ └╥┘┌─┐   \n",
      "q33_1: ─░──╫─┤M├───\n",
      "        ░  ║ └╥┘┌─┐\n",
      "q33_2: ─░──╫──╫─┤M├\n",
      "        ░  ║  ║ └╥┘\n",
      " c0: 3/════╩══╩══╩═\n",
      "           0  1  2 \n",
      "\n",
      "Circuit mcalcal_001\n",
      "       ┌───┐ ░ ┌─┐      \n",
      "q33_0: ┤ X ├─░─┤M├──────\n",
      "       └───┘ ░ └╥┘┌─┐   \n",
      "q33_1: ──────░──╫─┤M├───\n",
      "             ░  ║ └╥┘┌─┐\n",
      "q33_2: ──────░──╫──╫─┤M├\n",
      "             ░  ║  ║ └╥┘\n",
      " c0: 3/═════════╩══╩══╩═\n",
      "                0  1  2 \n",
      "\n",
      "Circuit mcalcal_010\n",
      "             ░ ┌─┐      \n",
      "q33_0: ──────░─┤M├──────\n",
      "       ┌───┐ ░ └╥┘┌─┐   \n",
      "q33_1: ┤ X ├─░──╫─┤M├───\n",
      "       └───┘ ░  ║ └╥┘┌─┐\n",
      "q33_2: ──────░──╫──╫─┤M├\n",
      "             ░  ║  ║ └╥┘\n",
      " c0: 3/═════════╩══╩══╩═\n",
      "                0  1  2 \n",
      "\n",
      "Circuit mcalcal_011\n",
      "       ┌───┐ ░ ┌─┐      \n",
      "q33_0: ┤ X ├─░─┤M├──────\n",
      "       ├───┤ ░ └╥┘┌─┐   \n",
      "q33_1: ┤ X ├─░──╫─┤M├───\n",
      "       └───┘ ░  ║ └╥┘┌─┐\n",
      "q33_2: ──────░──╫──╫─┤M├\n",
      "             ░  ║  ║ └╥┘\n",
      " c0: 3/═════════╩══╩══╩═\n",
      "                0  1  2 \n",
      "\n",
      "Circuit mcalcal_100\n",
      "             ░ ┌─┐      \n",
      "q33_0: ──────░─┤M├──────\n",
      "             ░ └╥┘┌─┐   \n",
      "q33_1: ──────░──╫─┤M├───\n",
      "       ┌───┐ ░  ║ └╥┘┌─┐\n",
      "q33_2: ┤ X ├─░──╫──╫─┤M├\n",
      "       └───┘ ░  ║  ║ └╥┘\n",
      " c0: 3/═════════╩══╩══╩═\n",
      "                0  1  2 \n",
      "\n",
      "Circuit mcalcal_101\n",
      "       ┌───┐ ░ ┌─┐      \n",
      "q33_0: ┤ X ├─░─┤M├──────\n",
      "       └───┘ ░ └╥┘┌─┐   \n",
      "q33_1: ──────░──╫─┤M├───\n",
      "       ┌───┐ ░  ║ └╥┘┌─┐\n",
      "q33_2: ┤ X ├─░──╫──╫─┤M├\n",
      "       └───┘ ░  ║  ║ └╥┘\n",
      " c0: 3/═════════╩══╩══╩═\n",
      "                0  1  2 \n",
      "\n",
      "Circuit mcalcal_110\n",
      "             ░ ┌─┐      \n",
      "q33_0: ──────░─┤M├──────\n",
      "       ┌───┐ ░ └╥┘┌─┐   \n",
      "q33_1: ┤ X ├─░──╫─┤M├───\n",
      "       ├───┤ ░  ║ └╥┘┌─┐\n",
      "q33_2: ┤ X ├─░──╫──╫─┤M├\n",
      "       └───┘ ░  ║  ║ └╥┘\n",
      " c0: 3/═════════╩══╩══╩═\n",
      "                0  1  2 \n",
      "\n",
      "Circuit mcalcal_111\n",
      "       ┌───┐ ░ ┌─┐      \n",
      "q33_0: ┤ X ├─░─┤M├──────\n",
      "       ├───┤ ░ └╥┘┌─┐   \n",
      "q33_1: ┤ X ├─░──╫─┤M├───\n",
      "       ├───┤ ░  ║ └╥┘┌─┐\n",
      "q33_2: ┤ X ├─░──╫──╫─┤M├\n",
      "       └───┘ ░  ║  ║ └╥┘\n",
      " c0: 3/═════════╩══╩══╩═\n",
      "                0  1  2 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Durchlaufe alle acht Basiszustände\n",
    "\n",
    "qr = QuantumRegister(3)\n",
    "meas_calibs, state_labels = complete_meas_cal(qr=qr, circlabel='mcal')\n",
    "\n",
    "for circuit in meas_calibs:\n",
    "    print('Circuit',circuit.name)\n",
    "    print(circuit)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a8faee53-8ed3-47d7-904e-e226ebd19e82",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\n",
       "\n",
       "\\begin{bmatrix}\n",
       "0.9776 & 0.0312 & 0.0404 & 0.0015 & 0.035 & 0.0014 & 0.0014 & 0  \\\\\n",
       " 0.0078 & 0.9573 & 0.0006 & 0.0407 & 0.0003 & 0.0341 & 0 & 0.0018  \\\\\n",
       " 0.0107 & 0.0002 & 0.9477 & 0.0323 & 0.0003 & 0 & 0.0368 & 0.0012  \\\\\n",
       " 0 & 0.0065 & 0.0079 & 0.9216 & 0 & 0.0003 & 0.0002 & 0.0353  \\\\\n",
       " 0.0039 & 0.0002 & 0 & 0 & 0.9482 & 0.0319 & 0.038 & 0.0014  \\\\\n",
       " 0 & 0.0046 & 0 & 0.0001 & 0.0071 & 0.9271 & 0.0003 & 0.0358  \\\\\n",
       " 0 & 0 & 0.0034 & 0.0001 & 0.0091 & 0.0004 & 0.9155 & 0.0321  \\\\\n",
       " 0 & 0 & 0 & 0.0037 & 0 & 0.0048 & 0.0078 & 0.8924  \\\\\n",
       " \\end{bmatrix}\n",
       "$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Berechne Korrekturmatrix\n",
    "\n",
    "t_qc = transpile(meas_calibs, device)\n",
    "cal_results = device.run(t_qc, shots=10000).result()\n",
    "\n",
    "meas_fitter = CompleteMeasFitter(cal_results, state_labels, circlabel='mcal')\n",
    "array_to_latex(meas_fitter.cal_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6646671c-e05a-4fc8-87b5-a3ad5bd6647a",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter Objekt erstellen\n",
    "meas_filter = meas_fitter.filter\n",
    "\n",
    "# Neue Ergebnisse nach Mitigation\n",
    "mitigated_results = meas_filter.apply(results)\n",
    "mitigated_counts = mitigated_results.get_counts()\n",
    "\n",
    "noisy_counts = results.get_counts()\n",
    "plot_histogram([noisy_counts, mitigated_counts], legend=['noisy', 'mitigated'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b95c067b-df47-4926-a6ad-174a0c6125c5",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SamplerResult(quasi_dists=[{'000': 0.5049, '111': 0.4951}], metadata=[{'header_metadata': {'circuit_digest': 'e571fba44e9b117ebdec413e620d70b9f08dd401f3f8f9ed1dfd9082bb097cf4'}, 'shots': 10000}])\n"
     ]
    }
   ],
   "source": [
    "# Berechnung der Quasi-Probabilities mit IBMQ-QASM-Simulator\n",
    "\n",
    "service = QiskitRuntimeService(channel=\"ibm_quantum\", token=IBMQ_API_TOKEN)\n",
    "\n",
    "with Sampler(circuits=[qc1], service=service, options={ \"backend\": \"ibmq_qasm_simulator\" }) as sampler:\n",
    "    result = sampler(circuits=[0], shots=10000)\n",
    "    print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a7373dd9",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.21.2</td></tr><tr><td><code>qiskit-aer</code></td><td>0.10.4</td></tr><tr><td><code>qiskit-ibmq-provider</code></td><td>0.19.2</td></tr><tr><td><code>qiskit</code></td><td>0.37.2</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.13</td></tr><tr><td>Python compiler</td><td>Clang 12.0.0 </td></tr><tr><td>Python build</td><td>main, Aug 25 2022 18:29:29</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>8.0</td></tr><tr><td colspan='2'>Fri Sep 09 16:08:37 2022 CEST</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import qiskit.tools.jupyter\n",
    "%qiskit_version_table"
   ]
  }
 ],
 "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": "3.9.13"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
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 },
 "nbformat": 4,
 "nbformat_minor": 5
}