quntumcomputertest/hello.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import qiskit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.4.2'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qiskit.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_ibm_runtime import QiskitRuntimeService\n",
    "\n",
    "service = QiskitRuntimeService(channel=\"ibm_quantum\", token=\"e047c8fd398ecad4aacdd2d4086476bce18bcb9974535a0cd00bfe410f34630acaf1f06f6c3df7bfc35723a27d6ea25f0246defe92c3ebfb82209d66101ffbcd\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "backend = service.backend(name=\"ibm_kyiv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "127"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "backend.num_qubits # erwarteter output: 127 Qubits verfügbar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Matplotlib is building the font cache; this may take a moment.\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 287.294x200.667 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit import QuantumCircuit\n",
    "\n",
    "qc = QuantumCircuit(2) #2 Qubits\n",
    "\n",
    "qc.h(0) #hadamard Gatter auf Qubit 0 anwenden (superposition)\n",
    "qc.cx(0, 1) #verschränkter Zustand\n",
    "\n",
    "qc.draw(output=\"mpl\") #das quantum circuit malen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.quantum_info import Pauli\n",
    "\n",
    "zz = Pauli(\"ZZ\") #pauli matrix z x 2 (wirkt auf 2 qubit)\n",
    "zi = Pauli(\"ZI\") #pauli matrix z kombiniert mit identity matrix\n",
    "iz = Pauli(\"IZ\")\n",
    "xx = Pauli(\"XX\")\n",
    "xi = Pauli(\"XI\")\n",
    "ix = Pauli(\"IX\")\n",
    "\n",
    "obs = [zz, zi, iz, xx, xi, ix]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EstimatorResult(values=array([1.        , 0.03515625, 0.03515625, 1.        , 0.06054688,\n",
       "       0.06054688]), metadata=[{'shots': 1024, 'variance': 0.0, 'simulator_metadata': [{'num_bind_params': 1, 'runtime_parameter_bind': False, 'parallel_state_update': 8, 'parallel_shots': 1, 'sample_measure_time': 0.00038575, 'noise': 'ideal', 'batched_shots_optimization': False, 'remapped_qubits': False, 'active_input_qubits': [0, 1], 'device': 'CPU', 'time_taken': 0.000848583, 'measure_sampling': True, 'num_clbits': 2, 'max_memory_mb': 8192, 'input_qubit_map': [[1, 1], [0, 0]], 'num_qubits': 2, 'method': 'stabilizer', 'required_memory_mb': 0, 'fusion': {'enabled': False}}]}, {'shots': 1024, 'variance': 0.9987640380859375, 'simulator_metadata': [{'num_bind_params': 1, 'runtime_parameter_bind': False, 'parallel_state_update': 8, 'parallel_shots': 1, 'sample_measure_time': 0.00038575, 'noise': 'ideal', 'batched_shots_optimization': False, 'remapped_qubits': False, 'active_input_qubits': [0, 1], 'device': 'CPU', 'time_taken': 0.000848583, 'measure_sampling': True, 'num_clbits': 2, 'max_memory_mb': 8192, 'input_qubit_map': [[1, 1], [0, 0]], 'num_qubits': 2, 'method': 'stabilizer', 'required_memory_mb': 0, 'fusion': {'enabled': False}}]}, {'shots': 1024, 'variance': 0.9987640380859375, 'simulator_metadata': [{'num_bind_params': 1, 'runtime_parameter_bind': False, 'parallel_state_update': 8, 'parallel_shots': 1, 'sample_measure_time': 0.00038575, 'noise': 'ideal', 'batched_shots_optimization': False, 'remapped_qubits': False, 'active_input_qubits': [0, 1], 'device': 'CPU', 'time_taken': 0.000848583, 'measure_sampling': True, 'num_clbits': 2, 'max_memory_mb': 8192, 'input_qubit_map': [[1, 1], [0, 0]], 'num_qubits': 2, 'method': 'stabilizer', 'required_memory_mb': 0, 'fusion': {'enabled': False}}]}, {'shots': 1024, 'variance': 0.0, 'simulator_metadata': [{'num_bind_params': 1, 'runtime_parameter_bind': False, 'parallel_state_update': 8, 'parallel_shots': 1, 'sample_measure_time': 0.001163167, 'noise': 'ideal', 'batched_shots_optimization': False, 'remapped_qubits': False, 'active_input_qubits': [0, 1], 'device': 'CPU', 'time_taken': 0.001712458, 'measure_sampling': True, 'num_clbits': 2, 'max_memory_mb': 8192, 'input_qubit_map': [[1, 1], [0, 0]], 'num_qubits': 2, 'method': 'stabilizer', 'required_memory_mb': 0, 'fusion': {'enabled': False}}]}, {'shots': 1024, 'variance': 0.9963340759277344, 'simulator_metadata': [{'num_bind_params': 1, 'runtime_parameter_bind': False, 'parallel_state_update': 8, 'parallel_shots': 1, 'sample_measure_time': 0.001163167, 'noise': 'ideal', 'batched_shots_optimization': False, 'remapped_qubits': False, 'active_input_qubits': [0, 1], 'device': 'CPU', 'time_taken': 0.001712458, 'measure_sampling': True, 'num_clbits': 2, 'max_memory_mb': 8192, 'input_qubit_map': [[1, 1], [0, 0]], 'num_qubits': 2, 'method': 'stabilizer', 'required_memory_mb': 0, 'fusion': {'enabled': False}}]}, {'shots': 1024, 'variance': 0.9963340759277344, 'simulator_metadata': [{'num_bind_params': 1, 'runtime_parameter_bind': False, 'parallel_state_update': 8, 'parallel_shots': 1, 'sample_measure_time': 0.001163167, 'noise': 'ideal', 'batched_shots_optimization': False, 'remapped_qubits': False, 'active_input_qubits': [0, 1], 'device': 'CPU', 'time_taken': 0.001712458, 'measure_sampling': True, 'num_clbits': 2, 'max_memory_mb': 8192, 'input_qubit_map': [[1, 1], [0, 0]], 'num_qubits': 2, 'method': 'stabilizer', 'required_memory_mb': 0, 'fusion': {'enabled': False}}]}])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit_aer.primitives import Estimator\n",
    "\n",
    "estimator = Estimator()\n",
    "\n",
    "job = estimator.run([qc] * len(obs), obs)\n",
    "\n",
    "job.result()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as pl\n",
    "\n",
    "data = [\"ZZ\", \"ZI\", \"IZ\", \"XX\", \"XI\", \"IX\"]\n",
    "values = job.result().values\n",
    "\n",
    "pl.plot(data, values, '-o')\n",
    "pl.xlabel(\"obs\")\n",
    "pl.ylabel(\"erwartet\")\n",
    "pl.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}