noise modelling-CCZ  (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a9333c4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SErHb7fL9998bNGF4DA0Nyb59+wLvRP/ekpOTZd++fTI0NGT0qCGziGj/daTgXbt2DQUFBcjLy0NdXd2otW3btuHo0aP49NNPsXHjxlFrc+fOxdy5c6M5alj09vbi3LlzuHbtGrxeL6ZPn478/HyUlJRM+CWPGkY/g4xw+PBhASAej2fM2rx58yZ8q/3ss8+iPyxNyrT3gScz8qr75BcYI/7+++8oT0NTYdrfhZjMSMBLly41eBKaqufyMzCZx3P5CkzmwYBJNQZMqjFgUo0Bk2oMmFRjwKQaAybVGDCpxoBJNQZMqjFgUo0Bk2oMmFRjwKQaAybVGDCpxoBJNQZMqjFgUo0Bk2oMmFRjwKQaAybVGDCpxoBJNQZMqjFgUo0Bk2oMmFRjwKQaAybVGDCpxoBJNQZMqjFgUo0Bk2r/B+/l7eLsSEGXAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 203.885x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from ibm_quantum_widgets import CircuitComposer\n",
    "from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit\n",
    "from numpy import pi\n",
    "from qiskit.quantum_info import Statevector\n",
    "from qiskit.visualization import plot_state_qsphere\n",
    "from qiskit.circuit.library import MCMT\n",
    "from qiskit.circuit.library.standard_gates import SGate, TGate\n",
    "from qiskit import QuantumCircuit\n",
    "from qiskit_ibm_runtime.fake_provider import FakeAlmadenV2, FakeAuckland, FakeWashingtonV2, FakeCambridgeV2\n",
    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
    "import matplotlib.pyplot as plt\n",
    "from qiskit.quantum_info import SparsePauliOp\n",
    "from qiskit_ibm_runtime import EstimatorV2 as Estimator\n",
    "import numpy as np\n",
    "cczgate = QuantumCircuit(3)\n",
    "\n",
    "cczgate.ccz(0,1,2)\n",
    "\n",
    "cczgate.draw('mpl',style=\"bw\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "90765029",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1792.5x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc = QuantumCircuit(3)\n",
    "\n",
    "qc.h(2)\n",
    "qc.barrier()\n",
    "\n",
    "qc.h(2)\n",
    "qc.cx(1,2)\n",
    "qc.tdg(2)\n",
    "qc.cx(0,2)\n",
    "qc.t(2)\n",
    "qc.cx(1,2)\n",
    "qc.tdg(2)\n",
    "qc.cx(0,2)\n",
    "qc.t(2)\n",
    "qc.h(2)\n",
    "\n",
    "qc.barrier()\n",
    "\n",
    "qc.t(1)\n",
    "qc.cx(0,1)\n",
    "qc.tdg(1)\n",
    "qc.cx(0,1)\n",
    "qc.t(0)\n",
    "\n",
    "qc.barrier()\n",
    "qc.h(2)\n",
    "\n",
    "\n",
    "qc.draw('mpl',style='bw')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "623cddf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 956.385x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qcs = QuantumCircuit(3)\n",
    "\n",
    "\n",
    "\n",
    "qcs.cx(1,2)\n",
    "qcs.tdg(2)\n",
    "qcs.cx(0,2)\n",
    "qcs.t(2)\n",
    "qcs.cx(1,2)\n",
    "qcs.tdg(2)\n",
    "qcs.cx(0,2)\n",
    "qcs.t(2)\n",
    "\n",
    "qcs.barrier()\n",
    "\n",
    "\n",
    "csgate = SGate().control(1)\n",
    "\n",
    "qcs.append(csgate, [0, 1])\n",
    "\n",
    "\n",
    "\n",
    "qcs.draw('mpl', style='bw')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "61b17bb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                  ░      \n",
      "q_0: ──────────────■─────────────────────■────────░───■──\n",
      "                   │                     │        ░ ┌─┴─┐\n",
      "q_1: ──■───────────┼─────────■───────────┼────────░─┤ S ├\n",
      "     ┌─┴─┐┌─────┐┌─┴─┐┌───┐┌─┴─┐┌─────┐┌─┴─┐┌───┐ ░ └───┘\n",
      "q_2: ┤ X ├┤ Tdg ├┤ X ├┤ T ├┤ X ├┤ Tdg ├┤ X ├┤ T ├─░──────\n",
      "     └───┘└─────┘└───┘└───┘└───┘└─────┘└───┘└───┘ ░      \n"
     ]
    }
   ],
   "source": [
    "qcs = QuantumCircuit(3)\n",
    "\n",
    "\n",
    "\n",
    "qcs.cx(1,2)\n",
    "qcs.tdg(2)\n",
    "qcs.cx(0,2)\n",
    "qcs.t(2)\n",
    "qcs.cx(1,2)\n",
    "qcs.tdg(2)\n",
    "qcs.cx(0,2)\n",
    "qcs.t(2)\n",
    "\n",
    "qcs.barrier()\n",
    "\n",
    "\n",
    "csgate = SGate().control(1)\n",
    "\n",
    "qcs.append(csgate, [0, 1])\n",
    "\n",
    "\n",
    "\n",
    "qcs.draw('mpl', style='bw')\n",
    "print(qcs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dd7f0822",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from qiskit import QuantumCircuit, transpile\n",
    "from qiskit.quantum_info import Kraus, SuperOp\n",
    "from qiskit_aer import AerSimulator\n",
    "from qiskit.visualization import plot_histogram\n",
    "\n",
    "# Import from Qiskit Aer noise module\n",
    "from qiskit_aer.noise import (NoiseModel, QuantumError, ReadoutError,\n",
    "    pauli_error, depolarizing_error, thermal_relaxation_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2e9e35ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                     ░                       »\n",
      "   q_0: ──────────────■─────────────────────■────────░─────────────■─────────»\n",
      "                      │                     │        ░ ┌───┐┌───┐┌─┴─┐┌─────┐»\n",
      "   q_1: ──■───────────┼─────────■───────────┼────────░─┤ H ├┤ T ├┤ X ├┤ Tdg ├»\n",
      "        ┌─┴─┐┌─────┐┌─┴─┐┌───┐┌─┴─┐┌─────┐┌─┴─┐┌───┐ ░ └───┘└───┘└───┘└─────┘»\n",
      "   q_2: ┤ X ├┤ Tdg ├┤ X ├┤ T ├┤ X ├┤ Tdg ├┤ X ├┤ T ├─░───────────────────────»\n",
      "        └───┘└─────┘└───┘└───┘└───┘└─────┘└───┘└───┘ ░                       »\n",
      "meas: 3/═════════════════════════════════════════════════════════════════════»\n",
      "                                                                             »\n",
      "«                   ░ ┌─┐      \n",
      "«   q_0: ──■────────░─┤M├──────\n",
      "«        ┌─┴─┐┌───┐ ░ └╥┘┌─┐   \n",
      "«   q_1: ┤ X ├┤ H ├─░──╫─┤M├───\n",
      "«        └───┘└───┘ ░  ║ └╥┘┌─┐\n",
      "«   q_2: ───────────░──╫──╫─┤M├\n",
      "«                   ░  ║  ║ └╥┘\n",
      "«meas: 3/══════════════╩══╩══╩═\n",
      "«                      0  1  2 \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# System Specification\n",
    "n_qubits = 3\n",
    "qcs = QuantumCircuit(n_qubits)\n",
    "\n",
    "# Test Circuit\n",
    "qcs.cx(1, 2)\n",
    "qcs.tdg(2)\n",
    "qcs.cx(0, 2)\n",
    "qcs.t(2)\n",
    "qcs.cx(1, 2)\n",
    "qcs.tdg(2)\n",
    "qcs.cx(0, 2)\n",
    "qcs.t(2)\n",
    "\n",
    "qcs.barrier()\n",
    "\n",
    "# Controlled-S Gate (decomposed manually to avoid simulator error)\n",
    "qcs.h(1)\n",
    "qcs.t(1)\n",
    "qcs.cx(0, 1)\n",
    "qcs.tdg(1)\n",
    "qcs.cx(0, 1)\n",
    "qcs.h(1)\n",
    "\n",
    "# Add measurements\n",
    "qcs.measure_all()\n",
    "print(qcs)\n",
    "\n",
    "sim_ideal = AerSimulator()\n",
    "result_ideal = sim_ideal.run(qcs).result()\n",
    "plot_histogram(result_ideal.get_counts(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "837f4605",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NoiseModel:\n",
      "  Basis gates: ['cx', 'id', 'rz', 'sx', 'u1', 'u2', 'u3']\n",
      "  Instructions with noise: ['cx', 'u1', 'u3', 'reset', 'measure', 'u2']\n",
      "  All-qubits errors: ['reset', 'measure', 'u1', 'u2', 'u3', 'cx']\n"
     ]
    }
   ],
   "source": [
    "# Example error probabilities\n",
    "p_reset = 0.03\n",
    "p_meas = 0.1\n",
    "p_gate1 = 0.05\n",
    "\n",
    "# QuantumError objects\n",
    "error_reset = pauli_error([('X', p_reset), ('I', 1 - p_reset)])\n",
    "error_meas = pauli_error([('X',p_meas), ('I', 1 - p_meas)])\n",
    "error_gate1 = pauli_error([('X',p_gate1), ('I', 1 - p_gate1)])\n",
    "error_gate2 = error_gate1.tensor(error_gate1)\n",
    "\n",
    "# Add errors to noise model\n",
    "noise_bit_flip = NoiseModel()\n",
    "noise_bit_flip.add_all_qubit_quantum_error(error_reset, \"reset\")\n",
    "noise_bit_flip.add_all_qubit_quantum_error(error_meas, \"measure\")\n",
    "noise_bit_flip.add_all_qubit_quantum_error(error_gate1, [\"u1\", \"u2\", \"u3\"])\n",
    "noise_bit_flip.add_all_qubit_quantum_error(error_gate2, [\"cx\"])\n",
    "\n",
    "print(noise_bit_flip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "15affecc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create noisy simulator backend\n",
    "sim_noise = AerSimulator(noise_model=noise_bit_flip)\n",
    "\n",
    "# Transpile circuit for noisy basis gates\n",
    "qcs_tnoise = transpile(qcs, sim_noise)\n",
    "\n",
    "# Run and get counts\n",
    "result_bit_flip = sim_noise.run(qcs_tnoise).result()\n",
    "counts_bit_flip = result_bit_flip.get_counts(0)\n",
    "\n",
    "# Plot noisy output\n",
    "plot_histogram(counts_bit_flip,title=\"Bit-Flip Error Noise Model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5c033846",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# System Specification\n",
    "n_qubits = 3\n",
    "qcs = QuantumCircuit(n_qubits)\n",
    "\n",
    "# Test Circuit\n",
    "qcs.cx(1, 2)\n",
    "qcs.tdg(2)\n",
    "qcs.cx(0, 2)\n",
    "qcs.t(2)\n",
    "qcs.cx(1, 2)\n",
    "qcs.tdg(2)\n",
    "qcs.cx(0, 2)\n",
    "qcs.t(2)\n",
    "\n",
    "qcs.barrier()\n",
    "\n",
    "# Controlled-S Gate (decomposed manually to avoid simulator error)\n",
    "qcs.h(1)\n",
    "qcs.t(1)\n",
    "qcs.cx(0, 1)\n",
    "qcs.tdg(1)\n",
    "qcs.cx(0, 1)\n",
    "qcs.h(1)\n",
    "\n",
    "# Add measurements\n",
    "qcs.measure_all()\n",
    "\n",
    "# Thermal relaxation parameters (example values)\n",
    "T1s = np.random.normal(50e3, 10e3, 3)  # T1 values in microseconds\n",
    "T2s = np.random.normal(70e3, 10e3, 3)  # T2 values in microseconds\n",
    "\n",
    "# Truncate random T2s <= T1s\n",
    "T2s = np.array([min(T2s[j], 2 * T1s[j]) for j in range(3)])\n",
    "\n",
    "# Instruction times (in nanoseconds)\n",
    "time_u1 = 0   # virtual gate\n",
    "time_u2 = 50  # (single X90 pulse)\n",
    "time_u3 = 100 # (two X90 pulses)\n",
    "time_cx = 300\n",
    "time_reset = 1000  # 1 microsecond\n",
    "time_measure = 1000 # 1 microsecond\n",
    "\n",
    "# QuantumError objects\n",
    "errors_reset = [thermal_relaxation_error(t1, t2, time_reset)\n",
    "                for t1, t2 in zip(T1s, T2s)]\n",
    "errors_measure = [thermal_relaxation_error(t1, t2, time_measure)\n",
    "                  for t1, t2 in zip(T1s, T2s)]\n",
    "errors_u1  = [thermal_relaxation_error(t1, t2, time_u1)\n",
    "              for t1, t2 in zip(T1s, T2s)]\n",
    "errors_u2  = [thermal_relaxation_error(t1, t2, time_u2)\n",
    "              for t1, t2 in zip(T1s, T2s)]\n",
    "errors_u3  = [thermal_relaxation_error(t1, t2, time_u3)\n",
    "              for t1, t2 in zip(T1s, T2s)]\n",
    "errors_cx = [[thermal_relaxation_error(t1a, t2a, time_cx).expand(\n",
    "             thermal_relaxation_error(t1b, t2b, time_cx))\n",
    "              for t1a, t2a in zip(T1s, T2s)]\n",
    "               for t1b, t2b in zip(T1s, T2s)]\n",
    "\n",
    "# Add errors to noise model\n",
    "noise_thermal = NoiseModel()\n",
    "for j in range(3):\n",
    "    noise_thermal.add_quantum_error(errors_reset[j], \"reset\", [j])\n",
    "    noise_thermal.add_quantum_error(errors_measure[j], \"measure\", [j])\n",
    "    noise_thermal.add_quantum_error(errors_u1[j], \"u1\", [j])\n",
    "    noise_thermal.add_quantum_error(errors_u2[j], \"u2\", [j])\n",
    "    noise_thermal.add_quantum_error(errors_u3[j], \"u3\", [j])\n",
    "    for k in range(3):\n",
    "        noise_thermal.add_quantum_error(errors_cx[j][k], \"cx\", [j, k])\n",
    "\n",
    "# Create the noisy simulator backend\n",
    "sim_thermal = AerSimulator(noise_model=noise_thermal)\n",
    "\n",
    "# Transpile circuit for noisy basis gates\n",
    "circ_tthermal = transpile(qcs, sim_thermal)\n",
    "\n",
    "# Run the simulation with the noisy model\n",
    "result_thermal = sim_thermal.run(circ_tthermal).result()\n",
    "\n",
    "# Get the counts (measurement results)\n",
    "counts_thermal = result_thermal.get_counts()\n",
    "\n",
    "# Plot the noisy output\n",
    "plot_histogram(counts_thermal,title=\"thermal relaxation noise model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e5d7e919",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "from qiskit.visualization import plot_histogram\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# System Specification\n",
    "n_qubits = 3\n",
    "qcs = QuantumCircuit(n_qubits)\n",
    "\n",
    "# Test Circuit\n",
    "qcs.cx(1, 2)\n",
    "qcs.tdg(2)\n",
    "qcs.cx(0, 2)\n",
    "qcs.t(2)\n",
    "qcs.cx(1, 2)\n",
    "qcs.tdg(2)\n",
    "qcs.cx(0, 2)\n",
    "qcs.t(2)\n",
    "\n",
    "qcs.barrier()\n",
    "\n",
    "# Controlled-S Gate (decomposed manually to avoid simulator error)\n",
    "qcs.h(1)\n",
    "qcs.t(1)\n",
    "qcs.cx(0, 1)\n",
    "qcs.tdg(1)\n",
    "qcs.cx(0, 1)\n",
    "qcs.h(1)\n",
    "\n",
    "# Add measurements\n",
    "qcs.measure_all()\n",
    "\n",
    "# Thermal relaxation parameters (example values)\n",
    "T1s = np.random.normal(50e3, 10e3, 3)  # T1 values in microseconds\n",
    "T2s = np.random.normal(70e3, 10e3, 3)  # T2 values in microseconds\n",
    "\n",
    "# Truncate random T2s <= T1s\n",
    "T2s = np.array([min(T2s[j], 2 * T1s[j]) for j in range(3)])\n",
    "\n",
    "# Instruction times (in nanoseconds)\n",
    "time_u1 = 0   # virtual gate\n",
    "time_u2 = 50  # (single X90 pulse)\n",
    "time_u3 = 100 # (two X90 pulses)\n",
    "time_cx = 300\n",
    "time_reset = 1000  # 1 microsecond\n",
    "time_measure = 1000 # 1 microsecond\n",
    "\n",
    "# QuantumError objects for thermal relaxation errors\n",
    "errors_reset = [thermal_relaxation_error(t1, t2, time_reset)\n",
    "                for t1, t2 in zip(T1s, T2s)]\n",
    "errors_measure = [thermal_relaxation_error(t1, t2, time_measure)\n",
    "                  for t1, t2 in zip(T1s, T2s)]\n",
    "errors_u1  = [thermal_relaxation_error(t1, t2, time_u1)\n",
    "              for t1, t2 in zip(T1s, T2s)]\n",
    "errors_u2  = [thermal_relaxation_error(t1, t2, time_u2)\n",
    "              for t1, t2 in zip(T1s, T2s)]\n",
    "errors_u3  = [thermal_relaxation_error(t1, t2, time_u3)\n",
    "              for t1, t2 in zip(T1s, T2s)]\n",
    "errors_cx = [[thermal_relaxation_error(t1a, t2a, time_cx).expand(\n",
    "             thermal_relaxation_error(t1b, t2b, time_cx))\n",
    "              for t1a, t2a in zip(T1s, T2s)]\n",
    "               for t1b, t2b in zip(T1s, T2s)]\n",
    "\n",
    "# QuantumError objects for bit-flip error model\n",
    "p_reset = 0.03\n",
    "p_meas = 0.1\n",
    "p_gate1 = 0.05\n",
    "\n",
    "# Bit-flip errors\n",
    "error_reset = pauli_error([('X', p_reset), ('I', 1 - p_reset)])\n",
    "error_meas = pauli_error([('X', p_meas), ('I', 1 - p_meas)])\n",
    "error_gate1 = pauli_error([('X', p_gate1), ('I', 1 - p_gate1)])\n",
    "error_gate2 = error_gate1.tensor(error_gate1)\n",
    "\n",
    "# Add bit-flip errors to noise model\n",
    "noise_bit_flip = NoiseModel()\n",
    "noise_bit_flip.add_all_qubit_quantum_error(error_reset, \"reset\")\n",
    "noise_bit_flip.add_all_qubit_quantum_error(error_meas, \"measure\")\n",
    "noise_bit_flip.add_all_qubit_quantum_error(error_gate1, [\"u1\", \"u2\", \"u3\"])\n",
    "noise_bit_flip.add_all_qubit_quantum_error(error_gate2, [\"cx\"])\n",
    "\n",
    "# Add thermal relaxation errors to noise model\n",
    "noise_thermal = NoiseModel()\n",
    "for j in range(3):\n",
    "    noise_thermal.add_quantum_error(errors_reset[j], \"reset\", [j])\n",
    "    noise_thermal.add_quantum_error(errors_measure[j], \"measure\", [j])\n",
    "    noise_thermal.add_quantum_error(errors_u1[j], \"u1\", [j])\n",
    "    noise_thermal.add_quantum_error(errors_u2[j], \"u2\", [j])\n",
    "    noise_thermal.add_quantum_error(errors_u3[j], \"u3\", [j])\n",
    "    for k in range(3):\n",
    "        noise_thermal.add_quantum_error(errors_cx[j][k], \"cx\", [j, k])\n",
    "\n",
    "# Create the noisy simulator backend for thermal relaxation noise\n",
    "sim_thermal = AerSimulator(noise_model=noise_thermal)\n",
    "circ_tthermal = transpile(qcs, sim_thermal)\n",
    "result_thermal = sim_thermal.run(circ_tthermal).result()\n",
    "counts_thermal = result_thermal.get_counts()\n",
    "\n",
    "# Create the noisy simulator backend for bit-flip error noise\n",
    "sim_bit_flip = AerSimulator(noise_model=noise_bit_flip)\n",
    "circ_tbit_flip = transpile(qcs, sim_bit_flip)\n",
    "result_bit_flip = sim_bit_flip.run(circ_tbit_flip).result()\n",
    "counts_bit_flip = result_bit_flip.get_counts()\n",
    "\n",
    "# Get the union of all keys (outcomes) from both noise models\n",
    "all_outcomes = set(counts_thermal.keys()).union(set(counts_bit_flip.keys()))\n",
    "\n",
    "# Create lists for frequencies, padding with 0 where necessary\n",
    "thermal_values = [counts_thermal.get(outcome, 0) for outcome in all_outcomes]\n",
    "bit_flip_values = [counts_bit_flip.get(outcome, 0) for outcome in all_outcomes]\n",
    "\n",
    "# Convert the outcomes to a sorted list to keep the order consistent\n",
    "all_outcomes = sorted(list(all_outcomes))\n",
    "\n",
    "# Plot the results using bar chart\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "# Define the bar width and positions for the bars\n",
    "bar_width = 0.35\n",
    "index = np.arange(len(all_outcomes))\n",
    "\n",
    "# Plot thermal relaxation noise counts\n",
    "ax.bar(index, thermal_values, bar_width, label='Thermal Relaxation Noise', alpha=0.7)\n",
    "\n",
    "# Plot bit-flip error noise counts\n",
    "ax.bar(index + bar_width, bit_flip_values, bar_width, label='Bit-Flip Error Noise', alpha=0.7)\n",
    "\n",
    "ax.set_xlabel('Measurement Outcome')\n",
    "ax.set_ylabel('Frequency')\n",
    "ax.set_title('Comparison of Thermal Relaxation Noise vs Bit-Flip Error Noise')\n",
    "ax.set_xticks(index + bar_width / 2)\n",
    "ax.set_xticklabels(all_outcomes)\n",
    "ax.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "528b94b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Define the new circuit\n",
    "qc = QuantumCircuit(3)\n",
    "\n",
    "qc.h(2)\n",
    "qc.barrier()\n",
    "\n",
    "qc.h(2)\n",
    "qc.cx(1, 2)\n",
    "qc.tdg(2)\n",
    "qc.cx(0, 2)\n",
    "qc.t(2)\n",
    "qc.cx(1, 2)\n",
    "qc.tdg(2)\n",
    "qc.cx(0, 2)\n",
    "qc.t(2)\n",
    "qc.h(2)\n",
    "\n",
    "qc.barrier()\n",
    "\n",
    "qc.t(1)\n",
    "qc.cx(0, 1)\n",
    "qc.tdg(1)\n",
    "qc.cx(0, 1)\n",
    "qc.t(0)\n",
    "\n",
    "qc.barrier()\n",
    "qc.h(2)\n",
    "\n",
    "# Define the bit-flip noise model\n",
    "p_bit_flip = 0.05\n",
    "\n",
    "# Single-qubit bit-flip error\n",
    "bit_flip_single = pauli_error([('X', p_bit_flip), ('I', 1 - p_bit_flip)])\n",
    "\n",
    "# Two-qubit error (simplified model, same bit-flip applied independently to both qubits)\n",
    "bit_flip_two_qubit = bit_flip_single.tensor(bit_flip_single)\n",
    "\n",
    "# Noise model\n",
    "noise_bit_flip = NoiseModel()\n",
    "noise_bit_flip.add_all_qubit_quantum_error(bit_flip_single, [\"u1\", \"u2\", \"u3\"])\n",
    "noise_bit_flip.add_all_qubit_quantum_error(bit_flip_two_qubit, [\"cx\"])\n",
    "\n",
    "# Initialize backend and results lists\n",
    "backend = FakeJakartaV2()  # Backend with 7 qubits\n",
    "sim_noise = AerSimulator(noise_model=noise_bit_flip)\n",
    "\n",
    "depths = []\n",
    "gate_counts = []\n",
    "multiqubit_gate_counts = []\n",
    "noisy_depths = []\n",
    "noisy_gate_counts = []\n",
    "noisy_multiqubit_gate_counts = []\n",
    "\n",
    "levels = [str(x) for x in range(4)]\n",
    "\n",
    "for level in range(4):\n",
    "    # Generate pass manager for each optimization level\n",
    "    pass_manager = generate_preset_pass_manager(\n",
    "        optimization_level=level,\n",
    "        backend=backend,\n",
    "    )\n",
    "\n",
    "    # Transpile circuit for the backend\n",
    "    circ = pass_manager.run(qc)\n",
    "    circ = transpile(circ, backend=backend)\n",
    "\n",
    "    depths.append(circ.depth())\n",
    "    gate_counts.append(sum(circ.count_ops().values()))\n",
    "    multiqubit_gate_counts.append(circ.count_ops().get(\"cx\", 0))\n",
    "\n",
    "    # Apply noise model\n",
    "    circ_tnoise = transpile(circ, backend=sim_noise)  # Transpile for noise simulator\n",
    "    noisy_depths.append(circ_tnoise.depth())\n",
    "    noisy_gate_counts.append(sum(circ_tnoise.count_ops().values()))\n",
    "    noisy_multiqubit_gate_counts.append(circ_tnoise.count_ops().get(\"cx\", 0))\n",
    "\n",
    "# Plot results as bar charts\n",
    "index = np.arange(len(levels))\n",
    "bar_width = 0.35\n",
    "\n",
    "fig, ax = plt.subplots(3, 1, figsize=(12, 12))\n",
    "\n",
    "# Plot depths\n",
    "ax[0].bar(index, depths, bar_width, label=\"Depth (No Noise)\", alpha=0.7)\n",
    "ax[0].bar(index + bar_width, noisy_depths, bar_width, label=\"Depth (With Noise)\", alpha=0.7)\n",
    "ax[0].set_xlabel('Optimization Level')\n",
    "ax[0].set_ylabel('Circuit Depth')\n",
    "ax[0].set_title('Circuit Depth Comparison')\n",
    "ax[0].set_xticks(index + bar_width / 2)\n",
    "ax[0].set_xticklabels(levels)\n",
    "ax[0].legend()\n",
    "\n",
    "# Plot gate counts\n",
    "ax[1].bar(index, gate_counts, bar_width, label=\"Gate Count (No Noise)\", alpha=0.7)\n",
    "ax[1].bar(index + bar_width, noisy_gate_counts, bar_width, label=\"Gate Count (With Noise)\", alpha=0.7)\n",
    "ax[1].set_xlabel('Optimization Level')\n",
    "ax[1].set_ylabel('Gate Count')\n",
    "ax[1].set_title('Gate Count Comparison')\n",
    "ax[1].set_xticks(index + bar_width / 2)\n",
    "ax[1].set_xticklabels(levels)\n",
    "ax[1].legend()\n",
    "\n",
    "# Plot multi-qubit gate counts\n",
    "ax[2].bar(index, multiqubit_gate_counts, bar_width, label=\"Multi-Qubit Gate Count (No Noise)\", alpha=0.7)\n",
    "ax[2].bar(index + bar_width, noisy_multiqubit_gate_counts, bar_width, label=\"Multi-Qubit Gate Count (With Noise)\", alpha=0.7)\n",
    "ax[2].set_xlabel('Optimization Level')\n",
    "ax[2].set_ylabel('Multi-Qubit Gate Count')\n",
    "ax[2].set_title('Multi-Qubit Gate Count Comparison')\n",
    "ax[2].set_xticks(index + bar_width / 2)\n",
    "ax[2].set_xticklabels(levels)\n",
    "ax[2].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "65193756",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Define the circuit\n",
    "qcs = QuantumCircuit(3)\n",
    "\n",
    "qcs.cx(1, 2)\n",
    "qcs.tdg(2)\n",
    "qcs.cx(0, 2)\n",
    "qcs.t(2)\n",
    "qcs.cx(1, 2)\n",
    "qcs.tdg(2)\n",
    "qcs.cx(0, 2)\n",
    "qcs.t(2)\n",
    "\n",
    "qcs.barrier()\n",
    "\n",
    "csgate = SGate().control(1)\n",
    "qcs.append(csgate, [0, 1])  # Add Controlled-S Gate\n",
    "\n",
    "# Define the bit-flip noise model\n",
    "p_bit_flip = 0.05\n",
    "\n",
    "# Single-qubit bit-flip error\n",
    "bit_flip_single = pauli_error([('X', p_bit_flip), ('I', 1 - p_bit_flip)])\n",
    "\n",
    "# Two-qubit error (simplified model, same bit-flip applied independently to both qubits)\n",
    "bit_flip_two_qubit = bit_flip_single.tensor(bit_flip_single)\n",
    "\n",
    "# Noise model\n",
    "noise_bit_flip = NoiseModel()\n",
    "noise_bit_flip.add_all_qubit_quantum_error(bit_flip_single, [\"u1\", \"u2\", \"u3\"])\n",
    "noise_bit_flip.add_all_qubit_quantum_error(bit_flip_two_qubit, [\"cx\"])\n",
    "\n",
    "# Initialize backend and results lists\n",
    "backend = FakeJakartaV2()  # Backend with 7 qubits\n",
    "sim_noise = AerSimulator(noise_model=noise_bit_flip)\n",
    "\n",
    "depths = []\n",
    "gate_counts = []\n",
    "multiqubit_gate_counts = []\n",
    "noisy_depths = []\n",
    "noisy_gate_counts = []\n",
    "noisy_multiqubit_gate_counts = []\n",
    "\n",
    "levels = [str(x) for x in range(4)]\n",
    "\n",
    "for level in range(4):\n",
    "    # Generate pass manager for each optimization level\n",
    "    pass_manager = generate_preset_pass_manager(\n",
    "        optimization_level=level,\n",
    "        backend=backend,\n",
    "    )\n",
    "\n",
    "    # Transpile circuit for the backend\n",
    "    circ = pass_manager.run(qcs)\n",
    "    circ = transpile(circ, backend=backend)\n",
    "\n",
    "    depths.append(circ.depth())\n",
    "    gate_counts.append(sum(circ.count_ops().values()))\n",
    "    multiqubit_gate_counts.append(circ.count_ops().get(\"cx\", 0))\n",
    "\n",
    "    # Apply noise model\n",
    "    circ_tnoise = transpile(circ, backend=sim_noise)  # Transpile for noise simulator\n",
    "    noisy_depths.append(circ_tnoise.depth())\n",
    "    noisy_gate_counts.append(sum(circ_tnoise.count_ops().values()))\n",
    "    noisy_multiqubit_gate_counts.append(circ_tnoise.count_ops().get(\"cx\", 0))\n",
    "\n",
    "# Plot results as bar charts\n",
    "index = np.arange(len(levels))\n",
    "bar_width = 0.35\n",
    "\n",
    "fig, ax = plt.subplots(3, 1, figsize=(12, 12))\n",
    "\n",
    "# Plot depths\n",
    "ax[0].bar(index, depths, bar_width, label=\"Depth (No Noise)\", alpha=0.7)\n",
    "ax[0].bar(index + bar_width, noisy_depths, bar_width, label=\"Depth (With Noise)\", alpha=0.7)\n",
    "ax[0].set_xlabel('Optimization Level')\n",
    "ax[0].set_ylabel('Circuit Depth')\n",
    "ax[0].set_title('Circuit Depth Comparison')\n",
    "ax[0].set_xticks(index + bar_width / 2)\n",
    "ax[0].set_xticklabels(levels)\n",
    "ax[0].legend()\n",
    "\n",
    "# Plot gate counts\n",
    "ax[1].bar(index, gate_counts, bar_width, label=\"Gate Count (No Noise)\", alpha=0.7)\n",
    "ax[1].bar(index + bar_width, noisy_gate_counts, bar_width, label=\"Gate Count (With Noise)\", alpha=0.7)\n",
    "ax[1].set_xlabel('Optimization Level')\n",
    "ax[1].set_ylabel('Gate Count')\n",
    "ax[1].set_title('Gate Count Comparison')\n",
    "ax[1].set_xticks(index + bar_width / 2)\n",
    "ax[1].set_xticklabels(levels)\n",
    "ax[1].legend()\n",
    "\n",
    "# Plot multi-qubit gate counts\n",
    "ax[2].bar(index, multiqubit_gate_counts, bar_width, label=\"Multi-Qubit Gate Count (No Noise)\", alpha=0.7)\n",
    "ax[2].bar(index + bar_width, noisy_multiqubit_gate_counts, bar_width, label=\"Multi-Qubit Gate Count (With Noise)\", alpha=0.7)\n",
    "ax[2].set_xlabel('Optimization Level')\n",
    "ax[2].set_ylabel('Multi-Qubit Gate Count')\n",
    "ax[2].set_title('Multi-Qubit Gate Count Comparison')\n",
    "ax[2].set_xticks(index + bar_width / 2)\n",
    "ax[2].set_xticklabels(levels)\n",
    "ax[2].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b285facf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Define the CCZ gate circuit\n",
    "cczgate = QuantumCircuit(3)\n",
    "cczgate.ccz(0, 1, 2)\n",
    "\n",
    "# Define the bit-flip noise model\n",
    "p_bit_flip = 0.05\n",
    "\n",
    "# Single-qubit bit-flip error\n",
    "bit_flip_single = pauli_error([('X', p_bit_flip), ('I', 1 - p_bit_flip)])\n",
    "\n",
    "# Two-qubit error (simplified model, same bit-flip applied independently to both qubits)\n",
    "bit_flip_two_qubit = bit_flip_single.tensor(bit_flip_single)\n",
    "\n",
    "# Three-qubit error (simplified model)\n",
    "bit_flip_three_qubit = bit_flip_two_qubit.tensor(bit_flip_single)\n",
    "\n",
    "# Noise model\n",
    "noise_bit_flip = NoiseModel()\n",
    "noise_bit_flip.add_all_qubit_quantum_error(bit_flip_single, [\"u1\", \"u2\", \"u3\"])\n",
    "noise_bit_flip.add_all_qubit_quantum_error(bit_flip_two_qubit, [\"cx\"])\n",
    "noise_bit_flip.add_all_qubit_quantum_error(bit_flip_three_qubit, [\"ccx\", \"ccz\"])\n",
    "\n",
    "# Initialize backend and results lists\n",
    "backend = FakeJakartaV2()  # Backend with 7 qubits\n",
    "sim_noise = AerSimulator(noise_model=noise_bit_flip)\n",
    "\n",
    "depths = []\n",
    "gate_counts = []\n",
    "multiqubit_gate_counts = []\n",
    "noisy_depths = []\n",
    "noisy_gate_counts = []\n",
    "noisy_multiqubit_gate_counts = []\n",
    "\n",
    "levels = [str(x) for x in range(4)]\n",
    "\n",
    "for level in range(4):\n",
    "    # Generate pass manager for each optimization level\n",
    "    pass_manager = generate_preset_pass_manager(\n",
    "        optimization_level=level,\n",
    "        backend=backend,\n",
    "    )\n",
    "\n",
    "    # Transpile circuit for the backend\n",
    "    circ = pass_manager.run(cczgate)\n",
    "    circ = transpile(circ, backend=backend)\n",
    "\n",
    "    depths.append(circ.depth())\n",
    "    gate_counts.append(sum(circ.count_ops().values()))\n",
    "    multiqubit_gate_counts.append(circ.count_ops().get(\"cx\", 0) + circ.count_ops().get(\"ccx\", 0))\n",
    "\n",
    "    # Apply noise model\n",
    "    circ_tnoise = transpile(circ, backend=sim_noise)  # Transpile for noise simulator\n",
    "    noisy_depths.append(circ_tnoise.depth())\n",
    "    noisy_gate_counts.append(sum(circ_tnoise.count_ops().values()))\n",
    "    noisy_multiqubit_gate_counts.append(circ_tnoise.count_ops().get(\"cx\", 0) + circ_tnoise.count_ops().get(\"ccx\", 0))\n",
    "\n",
    "# Plot results as bar charts\n",
    "index = np.arange(len(levels))\n",
    "bar_width = 0.35\n",
    "\n",
    "fig, ax = plt.subplots(3, 1, figsize=(12, 12))\n",
    "\n",
    "# Plot depths\n",
    "ax[0].bar(index, depths, bar_width, label=\"Depth (No Noise)\", alpha=0.7)\n",
    "ax[0].bar(index + bar_width, noisy_depths, bar_width, label=\"Depth (With Noise)\", alpha=0.7)\n",
    "ax[0].set_xlabel('Optimization Level')\n",
    "ax[0].set_ylabel('Circuit Depth')\n",
    "ax[0].set_title('Circuit Depth Comparison')\n",
    "ax[0].set_xticks(index + bar_width / 2)\n",
    "ax[0].set_xticklabels(levels)\n",
    "ax[0].legend()\n",
    "\n",
    "# Plot gate counts\n",
    "ax[1].bar(index, gate_counts, bar_width, label=\"Gate Count (No Noise)\", alpha=0.7)\n",
    "ax[1].bar(index + bar_width, noisy_gate_counts, bar_width, label=\"Gate Count (With Noise)\", alpha=0.7)\n",
    "ax[1].set_xlabel('Optimization Level')\n",
    "ax[1].set_ylabel('Gate Count')\n",
    "ax[1].set_title('Gate Count Comparison')\n",
    "ax[1].set_xticks(index + bar_width / 2)\n",
    "ax[1].set_xticklabels(levels)\n",
    "ax[1].legend()\n",
    "\n",
    "# Plot multi-qubit gate counts\n",
    "ax[2].bar(index, multiqubit_gate_counts, bar_width, label=\"Multi-Qubit Gate Count (No Noise)\", alpha=0.7)\n",
    "ax[2].bar(index + bar_width, noisy_multiqubit_gate_counts, bar_width, label=\"Multi-Qubit Gate Count (With Noise)\", alpha=0.7)\n",
    "ax[2].set_xlabel('Optimization Level')\n",
    "ax[2].set_ylabel('Multi-Qubit Gate Count')\n",
    "ax[2].set_title('Multi-Qubit Gate Count Comparison')\n",
    "ax[2].set_xticks(index + bar_width / 2)\n",
    "ax[2].set_xticklabels(levels)\n",
    "ax[2].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00f3593a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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