xeb_qiskit.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# Uncomment the line below to install Aer package for custom depolarizing error\n",
    "# !pip install qiskit-aer"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T03:44:52.570992Z",
     "start_time": "2024-02-27T03:44:52.562148Z"
    }
   },
   "id": "3c96489c2cc9f2ff",
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "from qiskit import QuantumCircuit, QuantumRegister, transpile\n",
    "import numpy as np\n",
    "from qiskit.providers.fake_provider import FakeProvider\n",
    "from qiskit.circuit.library import CZGate, RXGate, RYGate, UnitaryGate, TGate, XGate, YGate, CPhaseGate, HGate\n",
    "from qiskit.quantum_info import Statevector, Operator, average_gate_fidelity\n",
    "import matplotlib.pyplot as plt\n",
    "from qiskit_aer import AerSimulator\n",
    "from qiskit_aer.noise import depolarizing_error, NoiseModel, thermal_relaxation_error, coherent_unitary_error, kraus_error\n",
    "from scipy.linalg import sqrtm"
   ],
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-03-05T02:07:23.040875Z",
     "start_time": "2024-03-05T02:07:22.261943Z"
    }
   },
   "id": "initial_id",
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# Define the gates\n",
    "X90, Y90 = RXGate(np.pi / 2), RYGate(np.pi / 2)\n",
    "T, X, Y, H = TGate(), XGate(), YGate(), HGate()\n",
    "W = UnitaryGate((X.to_matrix()+Y.to_matrix())/np.sqrt(2), label='W')\n",
    "SW = UnitaryGate(sqrtm(W.to_matrix()), label='SW')\n",
    "XY90 = UnitaryGate((X90.to_matrix() + Y90.to_matrix()) / np.sqrt(3), label=\"XY90\")\n",
    "XY90mm = UnitaryGate((-X90.to_matrix() - Y90.to_matrix()) / np.sqrt(3), label=\"XY90mm\")\n",
    "XY90pm = UnitaryGate((X90.to_matrix() - Y90.to_matrix()), label=\"XY90pm\")\n",
    "XY90mp = UnitaryGate((-X90.to_matrix() + Y90.to_matrix()), label=\"XY90mp\")\n",
    "CZ, CP = CZGate(), CPhaseGate(np.pi/2)\n",
    "\n",
    "# Possible gate sets\n",
    "gate_dict1 = {0: X90, 1: Y90, 2: T}  # https://arxiv.org/abs/1608.00263\n",
    "gate_dict2 = {0: X90, 1: Y90, 2: SW}  # https://www.nature.com/articles/s41586-019-1666-5\n",
    "gate_dict3 = {0: X90, 1: Y90, 2: XY90, 3: XY90mm, 4: XY90pm, 5: XY90mp} # https://arxiv.org/abs/2001.08343\n",
    "gate_dict4 = {0: X90, 1: Y90, 2: XY90} \n",
    "gate_sets = [gate_dict1, gate_dict2, gate_dict3, gate_dict4]\n",
    "\n",
    "# Choose the gate set\n",
    "gate_set_choice = 2 # 1, 2, 3 or 4\n",
    "gate_dict = gate_sets[gate_set_choice - 1]\n",
    "random_gates = len(gate_dict)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-05T02:08:20.333056Z",
     "start_time": "2024-03-05T02:08:20.311844Z"
    }
   },
   "id": "80d43b195d1f5c25",
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "AerSimulator('aer_simulator'\n             noise_model=<NoiseModel on ['h', 'SW', 'ry', 'rx']>)"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "depol_error1 = depolarizing_error(0.05, 1)\n",
    "depol_error2 = depolarizing_error(0.05, 2)\n",
    "t1_err = thermal_relaxation_error(20, 10, 20)\n",
    "coherent_err = coherent_unitary_error(RXGate(0.005*np.pi).to_matrix())\n",
    "noise_model = NoiseModel()\n",
    "# noise_model.add_all_qubit_quantum_error(depol_error1, [\"h\", \"rx\"]) \n",
    "# noise_model.add_all_qubit_quantum_error(depol_error2, [\"cz\", \"cp\"])\n",
    "noise_model.add_all_qubit_quantum_error(depol_error1, [\"h\", \"SW\", 'ry', 'rx'])\n",
    "backend = AerSimulator(noise_model=noise_model) \n",
    "backend"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-05T02:08:21.811034Z",
     "start_time": "2024-03-05T02:08:21.798558Z"
    }
   },
   "id": "590a1399907c2d53",
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-05T02:08:23.301613Z",
     "start_time": "2024-03-05T02:08:23.299998Z"
    }
   },
   "id": "3935f01f97055c65",
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# np.random.seed(335)\n",
    "sequences = 50 # Number of sequences\n",
    "max_depth = 500 # Maximum depth\n",
    "step = 50 # Step for depths to be tested\n",
    "depths = np.arange(1, max_depth + 1, step) # Depths to be tested\n",
    "n_avgs = 1000 # Number of shots\n",
    "np.random.seed(11211)\n",
    "num_qubits = 1 # Number of qubits\n",
    "qubits = QuantumRegister(num_qubits, \"q\") # Number of qubits\n",
    "\n",
    "sq_gates, counts_list, states_list, circuits_list = [], [], [], []  # 2D for 2 qubits\n",
    "\n",
    "# Generate sequences\n",
    "for s in range(sequences):  # For each sequence\n",
    "    counts_list.append([])\n",
    "    sq_gates.append([])\n",
    "    circuits_list.append([])\n",
    "    states_list.append([])\n",
    "    for i, d in enumerate(depths):  # For each maximum depth\n",
    "        sq_gates[s].append(np.zeros((2, d), dtype=int))\n",
    "        for q in range(num_qubits):  # For each qubit\n",
    "            # Generate random single qubit gates\n",
    "            # Start the sequence with a random gate\n",
    "            sq_gates[s][i][q][0] = np.random.randint(random_gates)\n",
    "        for d_ in range(1, d):  # For each growing depth (all cycles until maximum depth d)\n",
    "            for q in range(num_qubits):  # For each qubit\n",
    "                sq_gates[s][i][q][d_] = np.random.randint(random_gates)\n",
    "                # Make sure that the same gate is not applied twice in a row\n",
    "                while sq_gates[s][i][q][d_] == sq_gates[s][i][q][d_ - 1]:\n",
    "                    sq_gates[s][i][q][d_] = np.random.randint(random_gates)\n",
    "        # Define the circuit\n",
    "        qc = QuantumCircuit(qubits)\n",
    "        # First cycle: apply Hadamard gatesthe o\n",
    "        # for q in range(num_qubits):\n",
    "        #     qc.append(H, [q])\n",
    "        if num_qubits == 2:\n",
    "            qc.append(CZ, qubits)\n",
    "        for d_ in range(d):  # Apply layers\n",
    "            for q in range(num_qubits):  # For each qubit, append single qubit gates\n",
    "                qc.append(gate_dict[sq_gates[s][i][q][d_]], [qubits[q]])\n",
    "            # Apply CZ gate\n",
    "            if num_qubits == 2:\n",
    "                qc.append(CZ, qubits)\n",
    "        # Store the statevector\n",
    "        states_list[s].append(Statevector(qc))\n",
    "        circuits_list[s].append(qc)\n",
    "        "
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-05T02:08:36.494654Z",
     "start_time": "2024-03-05T02:08:33.724392Z"
    }
   },
   "id": "89fe6c12be783006",
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2192.12x451.5 with 1 Axes>",
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Draw the circuit of your choice: first index is the sequence, second index is the depth index in depths array\n",
    "circuits_list[1][1].draw(output=\"mpl\", style='iqp')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-05T02:08:39.754060Z",
     "start_time": "2024-03-05T02:08:39.295442Z"
    }
   },
   "id": "ae064b87906d03e",
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# Execute circuit (transpiled) and store counts\n",
    "for s in range(sequences):\n",
    "    for i, d in enumerate(depths):\n",
    "        qc2 = transpile(circuits_list[s][i].measure_all(inplace=False), backend)\n",
    "        job = backend.run(qc2, shots=n_avgs)\n",
    "        counts = job.result().get_counts()\n",
    "\n",
    "        # Add missing keys to counts (Qiskit does not add all possible outcomes by default, only the ones carrying actual counts)\n",
    "        for key in [bin(i)[2:].zfill(num_qubits) for i in range(2 ** len(qubits))]:\n",
    "            if key not in counts.keys():\n",
    "                counts[key] = 0\n",
    "        # Add counts to list\n",
    "        counts_list[s].append(counts)\n",
    "        "
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-05T02:08:54.016082Z",
     "start_time": "2024-03-05T02:08:44.229649Z"
    }
   },
   "id": "578fa762573f9beb",
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def cross_entropy(p, q, epsilon=1e-15):\n",
    "    \"\"\"\n",
    "    Calculate cross entropy between two probability distributions.\n",
    "\n",
    "    Parameters:\n",
    "    - p: numpy array, the true probability distribution\n",
    "    - q: numpy array, the predicted probability distribution\n",
    "    - epsilon: small value to avoid taking the logarithm of zero\n",
    "\n",
    "    Returns:\n",
    "    - Cross entropy between p and q\n",
    "    \"\"\"\n",
    "    q = np.maximum(q, epsilon)  # Avoid taking the logarithm of zero\n",
    "\n",
    "    # print(f\"p: {p}, \\nq: {q}\")\n",
    "\n",
    "    x_entropy = -np.sum(p * np.log(q))\n",
    "\n",
    "    return x_entropy"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-05T02:08:54.616670Z",
     "start_time": "2024-03-05T02:08:54.614174Z"
    }
   },
   "id": "912fa6d25cd6681c",
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/y8/9zgwllj55dbbch4pxmg_g7jm0000gn/T/ipykernel_67206/3465738326.py:38: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  f_xeb = (xe_incoherent - xe_measured) / (xe_incoherent - xe_expected)\n",
      "/var/folders/y8/9zgwllj55dbbch4pxmg_g7jm0000gn/T/ipykernel_67206/3465738326.py:38: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  f_xeb = (xe_incoherent - xe_measured) / (xe_incoherent - xe_expected)\n"
     ]
    }
   ],
   "source": [
    "from scipy.optimize import curve_fit\n",
    "# Post-processing\n",
    "records = []\n",
    "incoherent_dist = np.ones(2** len(qubits)) / 2 ** len(qubits)\n",
    "fidelities = np.zeros((sequences, len(depths)))\n",
    "expected_probs = np.zeros((sequences, len(depths), 2 ** len(qubits)))\n",
    "measured_probs = np.zeros((sequences, len(depths), 2 ** len(qubits)))\n",
    "\n",
    "# Reconstruct sequence from gate indices (similar step to what is done in QUA)\n",
    "for s in range(sequences):\n",
    "    for j, d in enumerate(depths):\n",
    "        qc = QuantumCircuit(qubits)\n",
    "        # for q in range(num_qubits):\n",
    "        #     qc.append(H, [q])\n",
    "        if num_qubits == 2:\n",
    "            qc.append(CZ, qubits)\n",
    "        for k in range(d):\n",
    "            sq_gates1 = [gate_dict[sq_gates[s][j][q][k]] for q in range(num_qubits)]\n",
    "            # Retrieve random single qubit gates applied on both qubits\n",
    "            # Apply the gates on the state\n",
    "            for q, sq_gate in enumerate(sq_gates1):\n",
    "                qc.append(sq_gate, [q])\n",
    "            if num_qubits == 2:\n",
    "                qc.append(CZ, [0, 1])\n",
    "            # print(\"computed state: %s\" % state)\n",
    "        expected_probs[s, j] = Statevector(qc).probabilities()\n",
    "        # print(\"reconstructed probs\", expected_probs[s, j])\n",
    "        # print(\"state based probs\", states_list[s][j].probabilities())\n",
    "        measured_probs[s, j] = (\n",
    "            np.array([counts_list[s][j][bin(i)[2:].zfill(num_qubits)] for i in range(2 ** num_qubits)])\n",
    "            / n_avgs\n",
    "        )\n",
    "        # print(\"measured\", measured_probs[s, j])\n",
    "        xe_incoherent = cross_entropy(incoherent_dist, expected_probs[s, j])\n",
    "        xe_measured = cross_entropy(measured_probs[s, j], expected_probs[s, j])\n",
    "        xe_expected = cross_entropy(expected_probs[s, j], expected_probs[s, j])\n",
    "\n",
    "        f_xeb = (xe_incoherent - xe_measured) / (xe_incoherent - xe_expected)\n",
    "        fidelities[s, j] = f_xeb\n",
    "        # print(f\"seq {s + 1}, depth {depths[j]}: f_xeb = {f_xeb}\")\n",
    "        records += [\n",
    "            {\n",
    "                \"sequence\": s,\n",
    "                \"depth\": depths[j],\n",
    "                \"pure_probs\": expected_probs[s, j],\n",
    "                # \"pure_probs\": states_list[s][j].probabilities(),\n",
    "                \"sampled_probs\": measured_probs[s, j],\n",
    "                \"circuit\": circuits_list[s][j],\n",
    "            }\n",
    "        ]\n",
    "\n",
    "for record in records:\n",
    "    e_u = np.sum(record[\"pure_probs\"] ** 2)\n",
    "    u_u = np.sum(record[\"pure_probs\"]) / 2 ** len(qubits)\n",
    "    m_u = np.sum(record[\"pure_probs\"] * record[\"sampled_probs\"])\n",
    "    record.update(e_u=e_u, u_u=u_u, m_u=m_u)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-05T02:08:57.681006Z",
     "start_time": "2024-03-05T02:08:55.196095Z"
    }
   },
   "id": "8084675369e59f09",
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 8 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results\n",
    "def create_subplot(data, subplot_number, title):\n",
    "    plt.subplot(subplot_number)\n",
    "    plt.pcolor(depths, range(sequences), np.abs(data), vmin=0., vmax=1.0)\n",
    "    ax = plt.gca()\n",
    "    ax.set_title(title)\n",
    "    ax.set_xlabel(\"Circuit depth\")\n",
    "    ax.set_ylabel(\"Sequences\")\n",
    "    ax.set_xticks(depths)\n",
    "    ax.set_yticks(np.arange(1, sequences + 1))\n",
    "    plt.colorbar()\n",
    "\n",
    "titles, data = [], []\n",
    "\n",
    "for i in range(2 ** num_qubits):\n",
    "    titles.append(f\"<{bin(i)[2:].zfill(num_qubits)}> Measured\")\n",
    "    titles.append(f\"<{bin(i)[2:].zfill(num_qubits)}> Expected\")\n",
    "    data.append(measured_probs[:, :, i])\n",
    "    data.append(expected_probs[:, :, i])\n",
    "\n",
    "plot_number = [241, 242, 243, 244, 245, 246, 247, 248]\n",
    "\n",
    "k = 0\n",
    "for title, d, n in zip(titles, data, plot_number):\n",
    "\n",
    "    qubits_involved = \"\"\n",
    "    for q in qubits:\n",
    "        qubits_involved += f\"q{q}-\"\n",
    "    plt.suptitle(f\"XEB for \"+ qubits_involved+f\", shots: {n_avgs}, sequences: {sequences}, max-depth: {max_depth}, CZ: {True if num_qubits == 2 else False}\")\n",
    "    create_subplot(d, n, title)\n",
    "    plt.subplots_adjust(wspace=1.1)\n",
    "    k+=1\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-05T02:08:58.421923Z",
     "start_time": "2024-03-05T02:08:57.682260Z"
    }
   },
   "id": "5224ba5fb5c5096e",
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "     sequence  depth                                 pure_probs  \\\n0           0      1   [0.5000000000000001, 0.4999999999999999]   \n1           0     51   [0.2464498950832288, 0.7535501049167802]   \n2           0    101  [0.8837170821851621, 0.11628291781485442]   \n3           0    151  [0.7990880103982396, 0.20091198960177292]   \n4           0    201  [0.12417113478636083, 0.8758288652136657]   \n..        ...    ...                                        ...   \n495        49    251   [0.6442942986393816, 0.3557057013606429]   \n496        49    301  [0.5157027992967331, 0.48429720070329924]   \n497        49    351  [0.7092187463100482, 0.29078125368999974]   \n498        49    401  [0.14075749140473942, 0.8592425085952927]   \n499        49    451   [0.2514999207183749, 0.7485000792816848]   \n\n      sampled_probs                                            circuit  \\\n0    [0.516, 0.484]  ((Instruction(name='rx', num_qubits=1, num_clb...   \n1    [0.279, 0.721]  ((Instruction(name='ry', num_qubits=1, num_clb...   \n2    [0.863, 0.137]  ((Instruction(name='ry', num_qubits=1, num_clb...   \n3    [0.788, 0.212]  ((Instruction(name='ry', num_qubits=1, num_clb...   \n4    [0.158, 0.842]  ((Instruction(name='ry', num_qubits=1, num_clb...   \n..              ...                                                ...   \n495  [0.634, 0.366]  ((Instruction(name='rx', num_qubits=1, num_clb...   \n496  [0.514, 0.486]  ((Instruction(name='ry', num_qubits=1, num_clb...   \n497  [0.698, 0.302]  ((Instruction(name='ry', num_qubits=1, num_clb...   \n498  [0.165, 0.835]  ((Instruction(name='ry', num_qubits=1, num_clb...   \n499  [0.231, 0.769]  ((Instruction(name='ry', num_qubits=1, num_clb...   \n\n          e_u  u_u       m_u         y         x     numerator   denominator  \n0    0.500000  0.5  0.500000  0.000000  0.000000  0.000000e+00  0.000000e+00  \n1    0.628575  0.5  0.612069  0.112069  0.128575  1.440933e-02  1.653161e-02  \n2    0.794478  0.5  0.778579  0.278579  0.294478  8.203516e-02  8.671706e-02  \n3    0.678907  0.5  0.672275  0.172275  0.178907  3.082120e-02  3.200781e-02  \n4    0.782495  0.5  0.757067  0.257067  0.282495  7.262004e-02  7.980324e-02  \n..        ...  ...       ...       ...       ...           ...           ...  \n495  0.541642  0.5  0.538671  0.038671  0.041642  1.610320e-03  1.734030e-03  \n496  0.500493  0.5  0.500440  0.000440  0.000493  2.168299e-07  2.432027e-07  \n497  0.587545  0.5  0.582851  0.082851  0.087545  7.253155e-03  7.664121e-03  \n498  0.758110  0.5  0.740692  0.240692  0.258110  6.212522e-02  6.662096e-02  \n499  0.623505  0.5  0.633693  0.133693  0.123505  1.651170e-02  1.525338e-02  \n\n[500 rows x 12 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sequence</th>\n      <th>depth</th>\n      <th>pure_probs</th>\n      <th>sampled_probs</th>\n      <th>circuit</th>\n      <th>e_u</th>\n      <th>u_u</th>\n      <th>m_u</th>\n      <th>y</th>\n      <th>x</th>\n      <th>numerator</th>\n      <th>denominator</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1</td>\n      <td>[0.5000000000000001, 0.4999999999999999]</td>\n      <td>[0.516, 0.484]</td>\n      <td>((Instruction(name='rx', num_qubits=1, num_clb...</td>\n      <td>0.500000</td>\n      <td>0.5</td>\n      <td>0.500000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>51</td>\n      <td>[0.2464498950832288, 0.7535501049167802]</td>\n      <td>[0.279, 0.721]</td>\n      <td>((Instruction(name='ry', num_qubits=1, num_clb...</td>\n      <td>0.628575</td>\n      <td>0.5</td>\n      <td>0.612069</td>\n      <td>0.112069</td>\n      <td>0.128575</td>\n      <td>1.440933e-02</td>\n      <td>1.653161e-02</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>101</td>\n      <td>[0.8837170821851621, 0.11628291781485442]</td>\n      <td>[0.863, 0.137]</td>\n      <td>((Instruction(name='ry', num_qubits=1, num_clb...</td>\n      <td>0.794478</td>\n      <td>0.5</td>\n      <td>0.778579</td>\n      <td>0.278579</td>\n      <td>0.294478</td>\n      <td>8.203516e-02</td>\n      <td>8.671706e-02</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>151</td>\n      <td>[0.7990880103982396, 0.20091198960177292]</td>\n      <td>[0.788, 0.212]</td>\n      <td>((Instruction(name='ry', num_qubits=1, num_clb...</td>\n      <td>0.678907</td>\n      <td>0.5</td>\n      <td>0.672275</td>\n      <td>0.172275</td>\n      <td>0.178907</td>\n      <td>3.082120e-02</td>\n      <td>3.200781e-02</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>201</td>\n      <td>[0.12417113478636083, 0.8758288652136657]</td>\n      <td>[0.158, 0.842]</td>\n      <td>((Instruction(name='ry', num_qubits=1, num_clb...</td>\n      <td>0.782495</td>\n      <td>0.5</td>\n      <td>0.757067</td>\n      <td>0.257067</td>\n      <td>0.282495</td>\n      <td>7.262004e-02</td>\n      <td>7.980324e-02</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>495</th>\n      <td>49</td>\n      <td>251</td>\n      <td>[0.6442942986393816, 0.3557057013606429]</td>\n      <td>[0.634, 0.366]</td>\n      <td>((Instruction(name='rx', num_qubits=1, num_clb...</td>\n      <td>0.541642</td>\n      <td>0.5</td>\n      <td>0.538671</td>\n      <td>0.038671</td>\n      <td>0.041642</td>\n      <td>1.610320e-03</td>\n      <td>1.734030e-03</td>\n    </tr>\n    <tr>\n      <th>496</th>\n      <td>49</td>\n      <td>301</td>\n      <td>[0.5157027992967331, 0.48429720070329924]</td>\n      <td>[0.514, 0.486]</td>\n      <td>((Instruction(name='ry', num_qubits=1, num_clb...</td>\n      <td>0.500493</td>\n      <td>0.5</td>\n      <td>0.500440</td>\n      <td>0.000440</td>\n      <td>0.000493</td>\n      <td>2.168299e-07</td>\n      <td>2.432027e-07</td>\n    </tr>\n    <tr>\n      <th>497</th>\n      <td>49</td>\n      <td>351</td>\n      <td>[0.7092187463100482, 0.29078125368999974]</td>\n      <td>[0.698, 0.302]</td>\n      <td>((Instruction(name='ry', num_qubits=1, num_clb...</td>\n      <td>0.587545</td>\n      <td>0.5</td>\n      <td>0.582851</td>\n      <td>0.082851</td>\n      <td>0.087545</td>\n      <td>7.253155e-03</td>\n      <td>7.664121e-03</td>\n    </tr>\n    <tr>\n      <th>498</th>\n      <td>49</td>\n      <td>401</td>\n      <td>[0.14075749140473942, 0.8592425085952927]</td>\n      <td>[0.165, 0.835]</td>\n      <td>((Instruction(name='ry', num_qubits=1, num_clb...</td>\n      <td>0.758110</td>\n      <td>0.5</td>\n      <td>0.740692</td>\n      <td>0.240692</td>\n      <td>0.258110</td>\n      <td>6.212522e-02</td>\n      <td>6.662096e-02</td>\n    </tr>\n    <tr>\n      <th>499</th>\n      <td>49</td>\n      <td>451</td>\n      <td>[0.2514999207183749, 0.7485000792816848]</td>\n      <td>[0.231, 0.769]</td>\n      <td>((Instruction(name='ry', num_qubits=1, num_clb...</td>\n      <td>0.623505</td>\n      <td>0.5</td>\n      <td>0.633693</td>\n      <td>0.133693</td>\n      <td>0.123505</td>\n      <td>1.651170e-02</td>\n      <td>1.525338e-02</td>\n    </tr>\n  </tbody>\n</table>\n<p>500 rows × 12 columns</p>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(records)\n",
    "df[\"y\"] = df[\"m_u\"] - df[\"u_u\"]\n",
    "df[\"x\"] = df[\"e_u\"] - df[\"u_u\"]\n",
    "\n",
    "df[\"numerator\"] = df[\"x\"] * df[\"y\"]\n",
    "df[\"denominator\"] = df[\"x\"] ** 2\n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-05T02:08:58.651115Z",
     "start_time": "2024-03-05T02:08:58.422754Z"
    }
   },
   "id": "f4a5e8e702cc4e87",
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Color by cycle depth\n",
    "import seaborn as sns\n",
    "\n",
    "colors = sns.cubehelix_palette(n_colors=len(depths))\n",
    "colors = {k: colors[i] for i, k in enumerate(depths)}\n",
    "\n",
    "_lines = []\n",
    "\n",
    "\n",
    "def per_cycle_depth(df):\n",
    "    fid_lsq = df[\"numerator\"].sum() / df[\"denominator\"].sum()\n",
    "\n",
    "    cycle_depth = df.name\n",
    "    xx = np.linspace(0, df[\"x\"].max())\n",
    "    (l,) = plt.plot(xx, fid_lsq * xx, color=colors[cycle_depth])\n",
    "    plt.scatter(df[\"x\"], df[\"y\"], color=colors[cycle_depth])\n",
    "\n",
    "    global _lines\n",
    "    _lines += [l]  # for legend\n",
    "    return pd.Series({\"fidelity\": fid_lsq})\n",
    "\n",
    "\n",
    "fids = df.groupby(\"depth\").apply(per_cycle_depth).reset_index()\n",
    "plt.xlabel(r\"$e_U - u_U$\", fontsize=18)\n",
    "plt.ylabel(r\"$m_U - u_U$\", fontsize=18)\n",
    "_lines = np.asarray(_lines)\n",
    "plt.legend(_lines[[0, -1]], depths[[0, -1]], loc=\"best\", title=\"Cycle depth\")\n",
    "plt.tight_layout()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-05T02:08:58.821601Z",
     "start_time": "2024-03-05T02:08:58.652504Z"
    }
   },
   "id": "3232d2abbcb65b5f",
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from typing import Tuple\n",
    "from scipy import optimize\n",
    "from scipy import stats\n",
    "\n",
    "\n",
    "# Fit the data\n",
    "def exponential_decay(cycle_depths: np.ndarray, a: float, layer_fid: float) -> np.ndarray:\n",
    "    \"\"\"An exponential decay for fitting.\n",
    "\n",
    "    This computes `a * layer_fid**cycle_depths`\n",
    "\n",
    "    Args:\n",
    "        cycle_depths: The various depths at which fidelity was estimated. This is the independent\n",
    "            variable in the exponential function.\n",
    "        a: A scale parameter in the exponential function.\n",
    "        layer_fid: The base of the exponent in the exponential function.\n",
    "    \"\"\"\n",
    "    return a * layer_fid**cycle_depths\n",
    "\n",
    "def _fit_exponential_decay(\n",
    "    cycle_depths: np.ndarray, fidelities: np.ndarray\n",
    ") -> Tuple[float, float, float, float]:\n",
    "    \"\"\"Fit an exponential model fidelity = a * layer_fid**x using nonlinear least squares.\n",
    "\n",
    "    This uses `exponential_decay` as the function to fit with parameters `a` and `layer_fid`.\n",
    "\n",
    "    Args:\n",
    "        cycle_depths: The various depths at which fidelity was estimated. Each element is `x`\n",
    "            in the fit expression.\n",
    "        fidelities: The estimated fidelities for each cycle depth. Each element is `fidelity`\n",
    "            in the fit expression.\n",
    "\n",
    "    Returns:\n",
    "        a: The first fit parameter that scales the exponential function, perhaps accounting for\n",
    "            state prep and measurement (SPAM) error.\n",
    "        layer_fid: The second fit parameters which serves as the base of the exponential.\n",
    "        a_std: The standard deviation of the `a` parameter estimate.\n",
    "        layer_fid_std: The standard deviation of the `layer_fid` parameter estimate.\n",
    "    \"\"\"\n",
    "    cycle_depths = np.asarray(cycle_depths)\n",
    "    fidelities = np.asarray(fidelities)\n",
    "\n",
    "    # Get initial guess by linear least squares with logarithm of model.\n",
    "    # This only works for positive fidelities. We use numpy fancy indexing\n",
    "    # with `positives` (an ndarray of bools).\n",
    "    positives = fidelities > 0\n",
    "    if np.sum(positives) <= 1:\n",
    "        # The sum of the boolean array is the number of `True` entries.\n",
    "        # For one or fewer positive values, we cannot perform the linear fit.\n",
    "        return 0, 0, np.inf, np.inf\n",
    "    cycle_depths_pos = cycle_depths[positives]\n",
    "    log_fidelities = np.log(fidelities[positives])\n",
    "\n",
    "    slope, intercept, _, _, _ = stats.linregress(cycle_depths_pos, log_fidelities)\n",
    "    layer_fid_0 = np.clip(np.exp(slope), 0, 1)\n",
    "    a_0 = np.clip(np.exp(intercept), 0, 1)\n",
    "\n",
    "    try:\n",
    "        (a, layer_fid), pcov = optimize.curve_fit(\n",
    "            exponential_decay,\n",
    "            cycle_depths,\n",
    "            fidelities,\n",
    "            p0=(a_0, layer_fid_0),\n",
    "            bounds=((0, 0), (1, 1)),\n",
    "        )\n",
    "    except ValueError:  # pragma: no cover\n",
    "        return 0, 0, np.inf, np.inf\n",
    "\n",
    "    a_std, layer_fid_std = np.sqrt(np.diag(pcov))\n",
    "    return a, layer_fid, a_std, layer_fid_std\n",
    "\n",
    "fit_google = True\n",
    "fit_log_entropy = True\n",
    "xx = np.linspace(0, fids[\"depth\"].max())\n",
    "# In XEB, we extract the depolarizing fidelity, which is\n",
    "# related to (but not equal to) the Pauli error.\n",
    "# For the latter, an error involves doing X, Y, or Z with E_PAULI/3\n",
    "# but for the former, an error involves doing I, X, Y, or Z with e_depol/4\n",
    "try:\n",
    "    a, layer_fid, a_std, layer_fid_std = _fit_exponential_decay(fids[\"depth\"], fids[\"fidelity\"])\n",
    "    plt.plot(xx, exponential_decay(xx, a, layer_fid),\n",
    "         label='Fit (Google processing), layer fidelity={:.2f}'.format(layer_fid),\n",
    "         color='red')\n",
    "except:\n",
    "    print(\"Fit for Google processing data failed\")\n",
    "    fit_google = False\n",
    "Fxeb = np.mean(fidelities, axis=0)\n",
    "try:\n",
    "    a2, layer_fid2, a_std2, layer_fid_std2 = _fit_exponential_decay(depths[1:], Fxeb[1:])\n",
    "    plt.plot(xx[1:], exponential_decay(xx[1:], a2, layer_fid2),\n",
    "        label=\"Fit (Log-entropy processing), layer fidelity={:.2f}\".format(layer_fid),\n",
    "        color=\"green\")\n",
    "    \n",
    "except:\n",
    "    print(\"Fit for Log-entropy processing data failed\")\n",
    "    fit_log_entropy = False\n",
    "    \n",
    "    \n",
    "plt.plot(fids[\"depth\"], fids[\"fidelity\"], marker=\"o\", label=\"Google processing\")\n",
    "\n",
    "plt.scatter(depths, Fxeb, marker = 'o',\n",
    "            color='green', label=\"Log-entropy processing\")\n",
    "\n",
    "plt.ylabel(\"Circuit fidelity\", fontsize=20)\n",
    "plt.xlabel(\"Cycle Depth $d$\", fontsize=20)\n",
    "plt.title(\"XEB Fidelity\")\n",
    "plt.legend(loc=\"best\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-05T02:08:58.947377Z",
     "start_time": "2024-03-05T02:08:58.822434Z"
    }
   },
   "id": "694142179ffa3e09",
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Google processing fit:\n",
      "Params:  0.8526271982064599 0.9999999999999998\n",
      "Error per cycle:  0.9999999999999998\n",
      "Log-entropy processing fit:\n",
      "Params:  0.9660093070475476 0.9997332216532392\n",
      "Error per cycle:  0.9997332216532392\n"
     ]
    }
   ],
   "source": [
    "if fit_google:\n",
    "    print(\"Google processing fit:\")\n",
    "    print(\"Params: \", a, layer_fid)\n",
    "    print(\"Error per cycle: \", layer_fid)\n",
    "if fit_log_entropy:\n",
    "    print(\"Log-entropy processing fit:\")\n",
    "    print(\"Params: \", a2, layer_fid2)\n",
    "    print(\"Error per cycle: \", layer_fid2)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-05T02:08:58.951462Z",
     "start_time": "2024-03-05T02:08:58.948492Z"
    }
   },
   "id": "39a25532f0822ac6",
   "execution_count": 13
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Currently used in the QM XEB example:\n",
      " [        nan -0.25333333  0.18       -0.285      -0.115      -0.12\n",
      "  0.1675     -0.26588235 -0.36625    -0.20571429]\n",
      "Linear XEB of Google:\n",
      " 0         NaN\n",
      "1   -0.028000\n",
      "2   -0.110000\n",
      "3    0.166667\n",
      "4   -0.012727\n",
      "5    0.082857\n",
      "6   -0.071304\n",
      "7    0.193333\n",
      "8    0.203478\n",
      "9    0.134286\n",
      "Name: fidelity, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(f\"Currently used in the QM XEB example:\\n {Fxeb}\") # Derived from the fSim paper\n",
    "print(f'Linear XEB of Google:\\n {fids[\"fidelity\"]}') # from Google's Supremacy paper"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-28T07:57:24.458627Z",
     "start_time": "2024-02-28T07:57:24.454444Z"
    }
   },
   "id": "722a7f8976d9c468",
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "absolute difference 0    0.760000\n",
      "1    0.068162\n",
      "2    0.169435\n",
      "3    0.663199\n",
      "4    0.031709\n",
      "5    0.001860\n",
      "6    0.067307\n",
      "7    0.018363\n",
      "8    0.025945\n",
      "9    0.015539\n",
      "Name: fidelity, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"absolute difference\", np.abs(fids[\"fidelity\"] - Fxeb))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T03:45:23.050918Z",
     "start_time": "2024-02-27T03:45:23.046698Z"
    }
   },
   "id": "beb8e7519445a8a5",
   "execution_count": 16
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x2a6479f50>]"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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Wum+SHKPOO3fu7Jjm6empbt26FahfeXGlPz4+Pk5jTrZt2yZjTK7PvXz5sr7//ntH28aNGzsNBqtTp44k6dKlS5Kk77//XnfffbcqVKjgaNO8eXN98803CgoK0tatW1W7dm0FBgY61mO329WxY0ft3r07x2H/63Xv3t3p8T333KOdO3fqwoUL2rp1qzw8PNS+ffsc23H69Gnt378/12V26NBBhw8f1vHjx5Wenq6dO3fq6aef1pUrVxyDl7/99lt17NhRkgq0DUW5T6+X1ymEX3/9VYGBgU7/so/uxMfHy8vLyzGmKFv233NcXJx27typjIwMRUVFObUJCwvTbbfdpri4OGVkZCghIUHdunVz+tbYrVu3XE8LZdu2bZsiIiLk4+Pj2BeVKlVSWFjYDU8TjBkzRqNGjdL58+eVmJioNWvWaNmyZZJyvpav/YZcvnx51ahRw3GYfceOHSpXrpzTWLOKFSuqffv2ea67oM/75ZdfdOLEiRy1bt26tSpVqpTjvcBms+W5vj/96U9q1aqV01WCn376qbp16yYfH58Cr+v7779XZGSkypcv71hO165d8zyylpvPPvtM0tX9vHLlyjzbPfvss47382v/ffjhhzds+z//8z+aMWOGGjRooEceeUR79uwpcN+uVxx1KoxLly5p165dat++veOIf2ZmpurXr69GjRo51hEREaEtW7boscce05IlS3Tw4EH17dtX0dHRf7gPhVHmTsf4+fkpODhYkhQcHKzQ0FD16tVLTz75pFatWuUY5JM9GOnac6HXOnXqVK7Tv/32W7322mv65Zdf5OfnJ39/f/n5+Ukq+PXdHh4eioqK0ocffqiXXnpJmzdv1rlz5xynYgrbt+wP2esHMtWuXbtA/cqLK/2pWbOm0wfItYO+cnPy5EnH/319fZ3mZZ+qyv5gTElJueEpgpSUFJ0+fVqBgYG5zj99+nSOgW/Xun4/1axZU8YYpaWlKSUlRcaYPM9bnzp1Ss2aNcsxvU2bNqpQoYK2bNmiunXrysvLSx07dlSjRo0UHx+vmjVr6sSJE44QUpBtKMp9er3q1aurYsWK+v33352m16tXTytWrHA8njdvnn7++WdJV//uqlevniMoZO/P8+fPO/42a9WqlWOdtWrV0vnz55WSkiK73Z6jxt7e3jlOLVwrJSVF69atyzGGR7rxAMMjR45owoQJ2rZtm7y9vdWwYUPH5ZPXv5Z9fHycHnt6ejrapKamqlq1ak5jZKT8X3cFeV52rSdNmuQ0Xizb9e8Fue3fa0VHR+vll1/W2bNndeLECR08eFATJkxwaV2pqak59mt+NbrWjh07NH/+fA0cOFC//PKL5syZ4xj/dL3bbrst1+m5ub5taGio2rdv7xi/9vbbbxdoOdcrjjoVxrlz55SVlaVFixblGJMkyfHlbNCgQfLz89PHH3+sadOmaerUqbLZbBo3bly+p6SLQ5kLIderWbOmJkyYoGHDhunVV191DAisUqWKpKsjs++4444cz8vtxXzkyBENHTpUnTt31sKFCx3f+JctW6Zvv/3WpX716tVLb7/9tuLi4rR27Vq1bNlS9evXL3TfJDneBJKSkhzns6X/vkAKq7D9ufa5H3zwgSOsXevafuancuXKuV7zv2nTJjVt2lSVK1fWHXfcob/97W+5Pv/6S/qul5qa6vRhk5SUJC8vL1WtWlWVK1dWxYoV9fe//z3X5/6///f/cp3u6+ur8PBwbdmyRbfeeqtatmypcuXKKSIiQvHx8fLz89Ntt93m+PAryDYU5T7NTefOnfXNN984jR0oX7680xt8tWrVHP+vWrWqzp49q8zMTKcgkv3GW716dUf4S0pKUqNGjZzWd/r0adWvX181a9ZUuXLllJyc7DQ/KytLZ8+ezbO/lStX1l133aWBAwfmmJfXEZSsrCwNHjxY5cqV04cffqiAgAB5e3vrwIEDWr16dZ7ryk316tV19uxZ2e12p6MB+b3uCvK87Fq/+OKLTpduZrtRqM7Nvffeq8mTJ+vLL7/Ur7/+qnr16jmWW9B1VatWTUlJSU7zjDH5HmmUpLNnz+qFF15Qo0aNFBsbq9TUVEVFRWnkyJFatWqVKlas6NL25MfPz0+33367fv3110Ivw4o65cbPz08eHh4aMGBArl9Asr9weHp66vHHH9fjjz+u5ORkbdq0SW+//baGDRumLVu2OB3BuhnK3OmY3HTt2lWRkZFau3at4wZHLVq0ULly5XTy5EkFBwc7/pUrV07Tp0/P9eYwu3fv1uXLlzVkyBCnUw7ZAST7m9H1iTk3DRs2VHBwsD799FNt3LjRacBsYfomSXfeeaek/x7qzJZ9uqiwCtsf6eqpJ+nqm8+1z01JSdHMmTNdCkhhYWH69ttvlZGR4Zj2008/afDgwUpMTFR4eLiOHz+umjVrOq1r69atWrx4cb6Hi68NkllZWfrss8/UokUL+fj4KDw8XBcvXpQxxmnZ+/fv17x58xwD4nKrfYcOHbRt2zZt375dERERkq7WKjExUV988YXjKIikAm1DUe7T3AwZMkR2u13jxo1z2tfZ0tPTna48CQ8Pl91uz3EkIvvDvFWrVmrRooXKly+fY5D0jh07dOzYMbVs2VJeXl5q2bKlvvrqK6c233zzzQ2vCgsPD9eBAwfUrFkzx74ICgrS+++/73Rl0rXOnj2rQ4cO6cEHH1Tz5s0dYWXz5s2S8j5SlJs2bdooMzPTqd8ZGRn5HoIvyPMaNmyomjVr6rfffnOqdd26dTV9+nTt3bu3wP2Urga2jh076uuvv9Znn32mqKgox99sQdfVpk0bbd682emU3rffflug09Fjx47V2bNn9dZbb6l8+fKqXbu2Jk+erMOHD+vVV191aVsKIi0tTQcPHszzS0JB3Mw63eizo1KlSgoICNAvv/zitI4mTZpo7ty5js+2Rx55RFOmTJF09Ut479699fjjj+v8+fNKS0srzC74Q8r8kZBs48aNU8+ePTVlyhStWrVK1atX16BBgzRr1iylpaUpIiJCJ0+e1KxZs+Th4aGmTZvmWEZgYKC8vb315ptv6sknn1RGRoZWrlypjRs3SpLjHHF2Kl67dq1atGjhOMJxvejoaL322mvy9PR0Go9QmL5JV7+N9+nTRzNmzFBmZqaaNWumTz75RD/99NMf2XWF7o909Rx1z5499dJLL+n3339XUFCQDh06pBkzZuhPf/pTrkdW8hITE6M+ffroqaeeUv/+/ZWRkaFZs2Y57o6YmZmppUuXauDAgXr66adVr149bdmyRYsWLVLfvn1zvUT7WjNnzpTdble9evX0r3/9S4cOHdKSJUskXb0/RuvWrRUTE6OYmBg1atRIP/74o+bMmaN27do5Dk9XqVJFCQkJ2rp1qwICAlS1alW1b99ekydPVlJSkuOyv/DwcGVmZioxMVEjRoxw9KF37975bkNR7tPcNGnSRNOnT9fo0aMVHR2thx9+WP7+/srMzFRCQoJWrFihpKQkDRo0SJJ09913KyIiQhMnTtSpU6cUEBCg+Ph4LVq0SA888IDjyoTBgwdr7ty5KleunDp37qzffvtNs2bNUuPGjdW7d29JV6+c6Nevn4YPH64HH3xQx44d06xZsyTlfZllTEyMHnnkEQ0ZMkSPPvqoKlSooOXLl+urr77S7Nmzc31OzZo1ddttt2nZsmWqW7euqlSpou+++04ffPCBpLzHzOSmTZs2ateunf76178qOTlZt912m/7+97/rzJkzNzx9WJDneXl5KTY2VhMmTHCcyjt37pzmz5+vkydP5nna7kaio6M1dOhQ2e12x7gdV9Y1dOhQffXVV/rLX/6iQYMG6ezZs46rL27k/fff14YNGzRhwgSne3Z06dJFf/7zn7VixQq1b99eXbt2dcw7cuSIdu7cmevyqlSp4nQjxevbJiUlafHixcrIyHD8rRbGzaxTlSpVtHfvXsXHx6t58+Y55j///PMaPHiwXnjhBfXs2VN2u13vvfeedu3apWeeeUbS1S9+7733nmrVqqXQ0FCdPHlSS5YsUXh4uDX3HLFkOKxFrh9pfL2pU6cam81mlixZ4pi2dOlS06NHDxMYGGjuuusu88ILL5jff//dMf/6K0fWr19v7rvvPhMcHGzatWtnnn32WRMfH2/8/f0do5NPnDhh/vznP5vAwEAzceJEY4zJdeR4cnKyCQwMNEOHDs21v/n1LTeZmZlm1qxZJjIy0jRv3twMHTrUzJ8//w9dHVPYfZXtypUrZu7cuaZz584mMDDQ3H333WbixInm7Nmzjja51S63fiQkJJi+ffua5s2bmzZt2pgxY8aYpKQkx/ykpCQzduxY06ZNGxMUFGS6detmFi1aZOx2e577LHsE+saNGx3b98ADD+S40uDChQvmtddeM3fffbcJDAw0nTp1MtOnT3e6Wmfr1q2mQ4cOJjAw0KxevdoxvXv37iYkJMRxFY0xxkRFRZmQkBCnqwwKug1FuU/z8ttvv5k333zT3HfffSYkJMS0aNHC3H///eb11183hw4dcmp78eJFM3XqVBMZGWkCAwMdfb52FL8xxvzzn/907OO2bdual19+2aSkpDi1+fLLL839999vAgMDTdeuXR1XXrz33nt5bsPu3bvNX/7yFxMaGmpCQkLMww8/bL766qsbbt9//vMf07dvXxMSEmLCw8PNY489ZjZv3mzuvfdeM3z4cGNMzqsTsl1/FcPFixfNK6+8YiIiIkxISIgZN26cmTJlyg2vjnHleZ9++ql54IEHTFBQkAkPDzdPP/202bdvn2N+blfk5dZPY67+7dx5550mOjo61z7lty5jru7v7Ndhx44dzerVq81dd92V59UxiYmJJjAw0AwZMiTX+WlpaaZz584mPDzcnDhxIt+rY2w2mxk8eLAxJverY5o2bWpat25tBgwYYLZs2ZLrOrPld3WMMcVXp+tfo2vWrHG87rdv357r39+WLVvMY489Zpo3b25atWplnnjiCbN9+3bH/CtXrpjZs2ebe+65xwQFBZk2bdqY8ePHO66au9k8jCllv4YDoNT6+uuvVbduXadvjvv379f999+v+fPnO135BcD9cToGQInx3Xffad26dRo5cqQaNGigEydOaMGCBWrYsKHatWtndfcAuIgjIQBKjPT0dM2aNUuff/65Tp06pWrVqikyMlIvvPBCvpefAnA/hBAAAGAJLtEFAACWIIQAAABLEEIAAIAl3PbqmOyfXfb09Mz3J44BAIB7MMYoKytL3t7e+d4h3G1DSPbdIgEAQMkTHByc72/RuG0IyU5PwcHBLv0EdH7sdrsSExOLfLkoHOrhfqiJe6Ee7oV65C97HxXkd9LcNoRkn4Lx8vIqlkIX13JRONTD/VAT90I93Av1yF9BhlIwMBUAAFiCEAIAACxBCAEAAJYghAAAAEsQQgAAgCUIIQAAwBKEEAAAYAlCCAAAsITb3qysuNizjHafuqwju46pbtWKCm9QQ16e/DYNAAA3W5kKIZ/tPq6XV+/RiXOXJZ2VJNWr6qOJUQG6N6ietZ0DAKCMKTOnYz7bfVzPLP3h/wLIf51ITdczS3/QZ7uPW9QzAADKpjIRQuxZRpPW7JXJZV72tElr9sqelVsLAABQHMpECIk/dEbHU9PznG8kHU9NV/yhMzevUwAAlHFlIoScOp93AClMOwAA8MeViRByS2WfIm0HAAD+uDIRQsIb1FC9qj7K60JcD129Sia8QY2b2S0AAMq0MhFCvDw9NDEqQJJyBJHsxxOjArhfCAAAN1GZCCGSdG9QPS3o21J1qlRwml63qo8W9G3JfUIAALjJytTNyu4NqqdO/rX1r6/iVaVOfe6YCgCAhcpUCJGunpoJuqWCQlrcKi8vL6u7AwBAmVVmTscAAAD3QggBAACWIIQAAABLEEIAAIAlCCEAAMAShBAAAGAJQggAALCEyyFk69ateuihh9SyZUu1bdtWkydPVnp67r8+u2nTJkVFRSkkJETdu3fXhg0b/nCHAQBA6eBSCDlz5oyGDBmiRx99VDt27NCqVasUHx+vd955J0fbw4cPa9iwYXruuee0Y8cODRs2TCNGjNDJkyeLrPMAAKDkcimE1KhRQ1u2bFHv3r3l4eGhlJQUXb58WTVq5Pz12VWrViksLEz33HOPvL291aNHD7Vu3VrLly8vss4DAICSy+XbtleqVEmS1L59e508eVJhYWHq3bt3jnYHDhyQzWZzmta4cWPt27fPpfXZ7XZXu1ig5RX1clE41MP9UBP3Qj3cC/XInyv7ptC/HfPFF18oNTVVI0eO1PDhw7V48WKn+RcuXJCvr6/TNB8fH128eNGl9SQmJha2i5YsF4VDPdwPNXEv1MO9UI+iUegQ4uPjIx8fH40aNUoPPfSQUlNTVbVqVcd8X1/fHANW09PT5efn59J6goODi/SH5ux2uxITE4t8uSgc6uF+qIl7oR7uhXrkL3sfFYRLIeSHH37QuHHjtHr1apUvX16SlJGRoXLlyuU46mGz2bRnzx6naQcOHFBQUJArq5SXl1exFLq4lovCoR7uh5q4F+rhXqhH0XBpYKq/v7/S09M1ffp0ZWRk6Pfff9e0adP04IMPOkJJtp49eyo+Pl7r1q1TZmam1q1bp/j4ePXq1atINwAAAJRMLoUQPz8/LV68WPv371fbtm3Vr18/3XXXXRo3bpwkKTQ0VKtXr5YkNWrUSPPmzdPChQvVunVrzZ8/X3PmzFGDBg2KfisAAECJ4/KYkMaNG+u9997LdV5CQoLT48jISEVGRhauZwAAoFTjtu0AAMAShBAAAGAJQggAALAEIQQAAFiCEAIAACxBCAEAAJYghAAAAEsQQgAAgCUIIQAAwBKEEAAAYAlCCAAAsAQhBAAAWIIQAgAALEEIAQAAliCEAAAASxBCAACAJQghAADAEoQQAABgCUIIAACwBCEEAABYghACAAAsQQgBAACWIIQAAABLEEIAAIAlCCEAAMAShBAAAGAJQggAALAEIQQAAFiCEAIAACxBCAEAAJYghAAAAEsQQgAAgCUIIQAAwBKEEAAAYAlCCAAAsAQhBAAAWIIQAgAALEEIAQAAliCEAAAASxBCAACAJQghAADAEoQQAABgCUIIAACwhLcrjfft26dp06Zpz549KleunNq2basxY8aoRo0aOdoOGjRIcXFx8vb+7ypmzZqlu++++4/3GgAAlHgFPhKSnp6uQYMGKTQ0VN99953Wrl2rlJQUjRs3Ltf2u3fv1rvvvquEhATHPwIIAADIVuAQcuzYMTVt2lRDhw5V+fLlVb16dfXp00fbt2/P0fbo0aNKTU1VQEBAkXYWAACUHgU+HdOwYUMtXrzYadrnn3+uwMDAHG0TExPl5+en2NhYJSYmqlatWhowYIAefPBBlztot9tdfk5BllfUy0XhUA/3Q03cC/VwL9Qjf67sG5fGhGQzxmjmzJnasGGDli5dmmN+RkaGQkJCFBsbqyZNmiguLk7Dhg2Tn5+funfv7tK6EhMTC9NFy5aLwqEe7oeauBfq4V6oR9HwMMYYV56QlpamsWPHas+ePVqwYIH8/f0L9LxJkyYpOTlZs2fPLlB7u92unTt3Kjg4WF5eXq50Md/lJiYmFvlyUTjUw/1QE/dCPdwL9chf9j4KCQnJdx+5dCTkyJEjeuqpp3TrrbdqxYoVuV4VI0krVqzIcdQjIyNDFSpUcGV1kiQvL69iKXRxLReFQz3cDzVxL9TDvVCPolHggampqanq37+/WrZsqXfffTfPACJdPVoyefJk7d27V1lZWdq4caPWrl2rPn36FEmnAQBAyVfgIyErV67UsWPHtH79en322WdO8xISEhQaGqpJkyapZ8+e6t+/vy5evKhnn31WycnJql+/vqZNm6awsLAi3wAAAFAyFTiEDBw4UAMHDsxzfkJCguP/Hh4eiomJUUxMzB/rHQAAKLW4bTsAALAEIQQAAFiCEAIAACxBCAEAAJYghAAAAEsQQgAAgCUIIQAAwBKEEAAAYAlCCAAAsAQhBAAAWIIQAgAALEEIAQAAliCEAAAASxBCAACAJQghAADAEoQQAABgCUIIAACwBCEEAABYghACAAAsQQgBAACWIIQAAABLEEIAAIAlCCEAAMAShBAAAGAJQggAALAEIQQAAFiCEAIAACxBCAEAAJYghAAAAEsQQgAAgCUIIQAAwBKEEAAAYAlCCAAAsAQhBAAAWIIQAgAALEEIAQAAliCEAAAASxBCAACAJQghAADAEoQQAABgCUIIAACwBCEEAABYghACAAAs4VII2bdvnwYOHKjw8HC1bdtWL774os6cOZNr202bNikqKkohISHq3r27NmzYUCQdBgAApUOBQ0h6eroGDRqk0NBQfffdd1q7dq1SUlI0bty4HG0PHz6sYcOG6bnnntOOHTs0bNgwjRgxQidPnizSzgPuwp5ltPVgsj7Z+bu2HkyWPctY3SUAcHveBW147NgxNW3aVEOHDpWXl5fKly+vPn366MUXX8zRdtWqVQoLC9M999wjSerRo4dWrlyp5cuXa/jw4UXXe8ANfLb7uCat2avjqemOafWq+mhiVIDuDapnYc8AwL0VOIQ0bNhQixcvdpr2+eefKzAwMEfbAwcOyGazOU1r3Lix9u3b53IH7Xa7y88pyPKKerkonJJej8/3nNDQf+7U9cc9TqSm65mlP2jeYyHqFljXkr4VVkmvSWlDPdwL9cifK/umwCHkWsYYzZw5Uxs2bNDSpUtzzL9w4YJ8fX2dpvn4+OjixYsurysxMbEwXbRsuSicklgPuzF66dPTOQKIJMe0l1b9qFoZx+Xl4XEzu1YkSmJNSjPq4V6oR9FwOYSkpaVp7Nix2rNnj5YuXSp/f/8cbXx9fZWenu40LT09XX5+fi53MDg4WF5eXi4/Ly92u12JiYlFvlwUTkmux7ZfkpV86cbjnJIvZelK1dvVqmHNm9SrP64k16Q0oh7uhXrkL3sfFYRLIeTIkSN66qmndOutt2rFihWqUaNGru1sNpv27NnjNO3AgQMKCgpyZXWSJC8vr2IpdHEtF4VTEuuRdOFKgduVtG2TSmZNSjPq4V6oR9Eo8NUxqamp6t+/v1q2bKl33303zwAiST179lR8fLzWrVunzMxMrVu3TvHx8erVq1eRdBpwB7dU9inSdgBQ1hQ4hKxcuVLHjh3T+vXr1apVK4WGhjr+SVJoaKhWr14tSWrUqJHmzZunhQsXqnXr1po/f77mzJmjBg0aFM9WABYIb1BD9ar6KK/RHh66epVMeIO8AzsAlGUFPh0zcOBADRw4MM/5CQkJTo8jIyMVGRlZ+J4Bbs7L00MTowL0zNIf5CE5DVDNDiYTowLk5VnyBqUCwM3AbduBP+DeoHpa0Lel6lZ1PuVSt6qPFvRtyX1CAOAGCnWJLoD/ujeonroE1FX8oTM6dT5dt1S+egqGIyAAcGOEEKAIeHl6qE2jknMZLgC4A07HAAAASxBCAACAJQghAADAEoQQAABgCUIIAACwBCEEAABYghACAAAsQQgBAACWIIQAAABLEEIAAIAlCCEAAMAShBAAAGAJQggAALAEIQQAAFiCEAIAACxBCAEAAJYghAAAAEsQQgAAgCUIIQAAwBKEEAAAYAlCCAAAsAQhBAAAWIIQAgAALEEIAQAAliCEAAAASxBCAACAJQghAADAEoQQAABgCUIIAACwBCEEAABYghACAAAsQQgBAACWIIQAAABLEEIAAIAlCCEAAMAShBAAAGAJQggAALAEIQQAAFiCEAIAACxBCAEAAJYodAg5c+aMunTpori4uDzbDBo0SMHBwQoNDXX827x5c2FXCQAAShHvwjzp+++/15gxY3TkyJEbttu9e7feffddhYeHF6pzAACg9HI5hKxatUqzZ8/WqFGjFBsbm2e7o0ePKjU1VQEBAX+og3a7/Q89P6/lFfVyUTjUw/1QE/dCPdwL9cifK/vGwxhjXFn46dOnVb16dXl7e8vf319///vfFRERkaPdunXrNGHCBIWGhioxMVG1atXSgAED9OCDDxZoPXa7XTt37nSlawAAwE2EhITIy8vrhm1cPhJSu3btArXLyMhQSEiIYmNj1aRJE8XFxWnYsGHy8/NT9+7dC7y+4ODgfDfCFXa7XYmJiUW+XBQO9XA/1MS9UA/3Qj3yl72PCqJQY0IKIjo6WtHR0Y7H7dq1U3R0tNavX+9SCPHy8iqWQhfXclE41MP9UBP3Qj3cC/UoGsV2ie6KFSu0fv16p2kZGRmqUKFCca0SAACUIMUWQtLS0jR58mTt3btXWVlZ2rhxo9auXas+ffoU1yoBAEAJUqSnY0JDQzVp0iT17NlT/fv318WLF/Xss88qOTlZ9evX17Rp0xQWFlaUqwQAACXUHwohP/30k9PjhIQEx/89PDwUExOjmJiYP7IKAABQSnHbdgAAYAlCCAAAsAQhBAAAWIIQAgAALEEIAQAAliCEAAAASxBCAACAJQghAADAEoQQAABgCUIIAACwBCEEAABYghACAAAsQQgBAACWIIQAAABLEEIAAIAlCCEAAMAShBAAAGAJQggAALAEIQQAAFiCEAIAACxBCAEAAJYghAAAAEsQQgAAgCUIIQAAwBKEEAAAYAlCCAAAsAQhBAAAWIIQAgAALEEIAQAAliCEAAAASxBCAACAJQghAADAEoQQAABgCUIIAACwBCEEAABYghACAAAsQQgBAACWIIQAAABLEEIAAIAlCCEAAMAShBAAAGAJQggAALBEoUPImTNn1KVLF8XFxeXZZtOmTYqKilJISIi6d++uDRs2FHZ1AACglClUCPn+++/Vp08fHTlyJM82hw8f1rBhw/Tcc89px44dGjZsmEaMGKGTJ08WurMAAKD0cDmErFq1SiNHjlRsbGy+7cLCwnTPPffI29tbPXr0UOvWrbV8+fJCdxYAAJQe3q4+oV27doqKipK3t/cNg8iBAwdks9mcpjVu3Fj79u1zaX12u93VLhZoeUW9XBQO9XA/1MS9UA/3Qj3y58q+cTmE1K5du0DtLly4IF9fX6dpPj4+unjxokvrS0xMdKm91ctF4VAP90NN3Av1cC/Uo2i4HEIKytfXV+np6U7T0tPT5efn59JygoOD5eXlVWT9stvtSkxMLPLlonCoh/uhJu6FergX6pG/7H1UEMUWQmw2m/bs2eM07cCBAwoKCnJpOV5eXsVS6OJaLgqHergfauJeqId7oR5Fo9juE9KzZ0/Fx8dr3bp1yszM1Lp16xQfH69evXoV1yoBAEAJUqQhJDQ0VKtXr5YkNWrUSPPmzdPChQvVunVrzZ8/X3PmzFGDBg2KcpUAAKCE+kOnY3766SenxwkJCU6PIyMjFRkZ+UdWAQAASilu2w4AACxBCAEAAJYghAAAAEsQQgAAgCUIIQAAwBKEEAAAYAlCCAAAsAQhBAAAWIIQAgAALEEIAQAAliCEAAAASxBCAACAJQghAADAEoQQAABgCUIIAACwBCEEAABYghACAAAsQQgBAACWIIQAAABLEEIAAIAlCCEAAMAShBAAAGAJQggAALAEIQQAAFiCEAIAACxBCAEAAJYghAAAAEsQQgAAgCUIIQAAwBKEEAAAYAlCCAAAsAQhBAAAWIIQAgAALEEIAQAAliCEAAAASxBCAACAJQghAADAEoQQAABgCUIIAACwBCEEAABYghACAAAsQQgBAACWIIQAAABLuBxCkpOTFRMTo7CwMEVEROjVV19VZmZmrm0HDRqk4OBghYaGOv5t3rz5D3caAACUfN6uPmHEiBGqU6eOvv32WyUlJemZZ57R+++/r0GDBuVou3v3br377rsKDw8vks4CAIDSw6UjIb/++qvi4+M1atQo+fr6qn79+oqJidGyZctytD169KhSU1MVEBBQZJ0FAAClh0tHQvbv369q1aqpTp06jmmNGjXSsWPHdO7cOVWpUsUxPTExUX5+foqNjVViYqJq1aqlAQMG6MEHH3Spg3a73aX2BV1eUS8XhUM93A81cS/Uw71Qj/y5sm9cCiEXLlyQr6+v07TsxxcvXnQKIRkZGQoJCVFsbKyaNGmiuLg4DRs2TH5+furevXuB15mYmOhKFy1fLgqHergfauJeqId7oR5Fw6UQUrFiRV26dMlpWvZjPz8/p+nR0dGKjo52PG7Xrp2io6O1fv16l0JIcHCwvLy8XOnmDdntdiUmJhb5clE41MP9UBP3Qj3cC/XIX/Y+KgiXQkiTJk2UkpKipKQk1apVS5J08OBB1a1bV5UrV3Zqu2LFihxHPTIyMlShQgVXVikvL69iKXRxLReFQz3cDzVxL9TDvVCPouHSwNQ77rhDrVq10muvvaa0tDQdPXpU8+fPz3WcR1pamiZPnqy9e/cqKytLGzdu1Nq1a9WnT58i6zwAACi5XL5Ed/bs2XrllVfUuXNneXp6Kjo6WjExMZKk0NBQTZo0ST179lT//v118eJFPfvss0pOTlb9+vU1bdo0hYWFFflGAACAksflEFKrVi3Nnj0713kJCQmO/3t4eCgmJsYRUAAAAK7FbdsBAIAlCCEAAMAShBAAAGAJQggAALAEIQQAAFiCEAIAACxBCAEAAJYghAAAAEsQQgAAgCUIIQAAwBKEEAAAYAlCCAAAsAQhBAAAWIIQAgAALEEIAQAAliCEAAAASxBCAACAJQghAADAEoQQAABgCUIIAACwhLfVHUDZZs8y2n3qso7sOqa6VSsqvEENeXl6WN0tAMBNQAiBZT7bfVwvr96jE+cuSzorSapX1UcTowJ0b1A9azsHACh2nI6BJT7bfVzPLP3h/wLIf51ITdczS3/QZ7uPW9QzAMDNQgjBTWfPMpq0Zq9MLvOyp01as1f2rNxaAABKC0IIbrr4Q2d0PDU9z/lG0vHUdMUfOnPzOgUAuOkIIbjpTp3PO4AUph0AoGQihOCmu6WyT5G2AwCUTIQQ3HThDWqoXlUf5XUhroeuXiUT3qDGzewWAOAmI4TgpvPy9NDEqABJyhFEsh9PjArgfiEAUMoRQmCJe4PqaUHflqpTpYLT9LpVfbSgb0vuEwIAZQA3K4Nl7g2qp07+tfWvr+JVpU597pgKAGUMIQSW8vL0UNAtFRTS4lZ5eXlZ3R0AwE3E6RgAAGAJQggAALAEp2NKKHuWUfyhMzp1Pl23VPZhLAVQCtmzjLb9kqztRy4pvUqy7mxUm9c5ShVCSAn02e7jmrRmr9Otz/n1WaB0yfE6j9vO6xylDqdjSpjsX5+9/rdX+PVZoPTgdY6yghBSgvDrs0Dpx+scZQkhpATh12dR3LLHIHx75JK2/ZLMB50FeJ2jLGFMSAnCr8+iODEGwT3wOndfDBQueoSQEoRfn0VxyR6DcP1xj+wxCNxK/+Ypja/z0nA1HyG9eBBCSpDsX589kZqe6/liD1397RV+fRauyG8MgoeujkHoElC3xH1wlESl7XVeGq7mI6QXH8aElCD8+iyKQ2kcg2DPMtp6MFmf7PxdWw+WrLEtpel1Xhqu8mGgcPFyOYQkJycrJiZGYWFhioiI0KuvvqrMzMxc227atElRUVEKCQlR9+7dtWHDhj/c4bIu+9dn61Z1PhTLr8+isErbGITPdh9Xu2nf6NFF2/Tc/+zUo4u2qd20b0rEB1620vA6Ly0f3qUxpEvuE9RdPh0zYsQI1alTR99++62SkpL0zDPP6P3339egQYOc2h0+fFjDhg3TW2+9pQ4dOuiLL77QiBEj9MUXX6hOnTpFtgFl0b1B9dQloG6JP8cK91CaxiCUpsPm2a/zbQdPa/vun9U6yFaiBkK68uHdplHNm9cxF5W2kC651ykyl46E/Prrr4qPj9eoUaPk6+ur+vXrKyYmRsuWLcvRdtWqVQoLC9M999wjb29v9ejRQ61bt9by5cuLrPNlmZenh9o0qqleIbepTaOaJeaNCe4newxCXn9BHrr6BuXuYxBKyzfva3l5eujOhjUVebuv7mxYsl7npeXDuzSFdMn9TpG5dCRk//79qlatmtORjEaNGunYsWM6d+6cqlSp4ph+4MAB2Ww2p+c3btxY+/btc6mDdrvdpfYFXV5RLxeFQz3cw0v3NdXQf+6Uh+T0Ie5xzXyZLLlzmbb9klygb97bDp7WnQ3d95v39Urqa6SWX7kCt3PnbWt1e1XVrVJBJ89dvuFA4Va3V3Xr7ZCuBvWXV+/JdxB6J/8/dsTNlf3gUgi5cOGCfH19naZlP7548aJTCMmtrY+Pjy5evOjKKpWYmOhSe6uXi8KhHtaqI2lkm2p6b+c5JV/Kckyv4eupJ0OqqM6VE9q584R1HSyA7UcuFazd7p/lc843/4ZupqS9RsoZo5q+nk5/T9er6eupcqlHtHPn0ZvYM9f1C/TVm1sv5zrPSOob4KPEH3fd3E4Vwu5Tl3XiXO7bIf03qP/rq3gF3VLhpvTJpRBSsWJFXbrk/ELPfuzn5+c03dfXV+npzt9K0tPTc7TLT3BwsLy8vFx6zo3Y7XYlJiYW+XJRONTDfYSESE/1MIr7JUnf7z2gVgGNFdGwVok5BZBeJVmK255vu9ZBNoWUsCMhJfU1Mrn8CQ39505JuR9hm/xAc7UKrHuzu+WykBCpQYMTemXtf5w+xOtV9dFL9zVVtxKwDZJ0ZNcxSWfzbVelTn2FtLi10OvJ/pstCJdCSJMmTZSSkqKkpCTVqlVLknTw4EHVrVtXlStXdmprs9m0Z88ep2kHDhxQUFCQK6uUl5dXsbzwimu5KBzq4R68vKS7GtdWxbTfFdK4domqyZ2Nahfo/holaXDntUria6RH89u0wNMzxyDIuiXsPiHS1W3pFnRriR0oLEl1q1YscLub9bfm0sDUO+64Q61atdJrr72mtLQ0HT16VPPnz9eDDz6Yo23Pnj0VHx+vdevWKTMzU+vWrVN8fLx69epVZJ0HgGyl6f4apcm9QfX03ehO+tdTd2rWIyH611N36rvRnUpUAMlWkgcKS+45CN3l+4TMnj1bmZmZ6ty5sx5++GFFRkYqJiZGkhQaGqrVq1dLujpgdd68eVq4cKFat26t+fPna86cOWrQoEHRbgEA/J/ScH+N0oir+dyDOwZ1l+8TUqtWLc2ePTvXeQkJCU6PIyMjFRkZWbieAUAhcB8dIG/ZQd1dTpHx2zEASp3sb94AcnKnoE4IAQCgjHGXoM4P2AEAAEsQQgAAgCUIIQAAwBKEEAAAYAlCCAAAsAQhBAAAWIIQAgAALEEIAQAAliCEAAAAS7jtHVONufpj3Ha7vUiXm728ol4uCod6uB9q4l6oh3uhHvnL3jfZn+M34mEK0soCGRkZSkxMtLobAACgEIKDg1W+fPkbtnHbEJKVlaXMzEx5enrKw4NfvwQAoCQwxigrK0ve3t7y9LzxqA+3DSEAAKB0Y2AqAACwBCEEAABYghACAAAsQQgBAACWIIQAAABLEEIAAIAlCCEAAMAShBAAAGCJMhVCkpOTFRMTo7CwMEVEROjVV19VZmam1d0qtfbt26eBAwcqPDxcbdu21YsvvqgzZ85Iknbt2qWHHnpIoaGh6tSpkz766COn565atUpdunRRSEiIevfurYSEBCs2oVSy2+3q16+fxowZ45hGPayRkpKiF198UREREWrdurViYmJ06tQpSdTECnv27NHjjz+usLAwtWvXTlOmTFFGRoYk6lFsTBnSt29f88ILL5iLFy+aI0eOmPvuu88sWrTI6m6VSpcuXTJt27Y1s2bNMpcvXzZnzpwxTz31lBkyZIhJSUkx4eHhZunSpebKlStmy5YtJjQ01OzatcsYY8y2bdtMaGio2bFjh8nIyDBLliwxERER5uLFixZvVekwc+ZM07RpUzN69GhjjKEeFurbt68ZOnSoSU1NNefPnzfPPvusGTx4MDWxgN1uN23btjUffPCBsdvt5vjx46Zbt25m7ty51KMYlZkjIb/++qvi4+M1atQo+fr6qn79+oqJidGyZcus7lqpdOzYMTVt2lRDhw5V+fLlVb16dfXp00fbt2/XF198oWrVqunxxx+Xt7e32rRpo6ioKEctPvroI913331q1aqVypUrpwEDBqh69epat26dxVtV8m3dulVffPGFunbt6phGPayxe/du7dq1S1OnTlWVKlVUqVIlTZ48WSNHjqQmFkhNTdXp06eVlZXl+PVXT09P+fr6Uo9iVGZCyP79+1WtWjXVqVPHMa1Ro0Y6duyYzp07Z2HPSqeGDRtq8eLF8vLyckz7/PPPFRgYqP3798tmszm1b9y4sfbt2ydJOnDgwA3no3CSk5M1fvx4TZ8+Xb6+vo7p1MMaP/74oxo3bqwPP/xQXbp0Ubt27TRt2jTVrl2bmligevXqGjBggKZNm6bg4GC1b99ed9xxhwYMGEA9ilGZCSEXLlxweuOV5Hh88eJFK7pUZhhjNGPGDG3YsEHjx4/PtRY+Pj6OOuQ3H67LysrSqFGjNHDgQDVt2tRpHvWwRmpqqn766ScdPnxYq1at0v/+7//q5MmTGj16NDWxQFZWlnx8fPTSSy9p586dWrt2rQ4ePKjZs2dTj2JUZkJIxYoVdenSJadp2Y/9/Pys6FKZkJaWpuHDh2vNmjVaunSp/P395evrq/T0dKd26enpjjrkNx+uW7hwocqXL69+/frlmEc9rFG+fHlJ0vjx41WpUiXVqlVLI0aM0KZNm2SMoSY32ZdffqnPP/9cjz32mMqXL68mTZpo6NCh+te//sVrpBiVmRDSpEkTpaSkKCkpyTHt4MGDqlu3ripXrmxhz0qvI0eO6M9//rPS0tK0YsUK+fv7S5JsNpv279/v1PbAgQNq0qSJpKu1utF8uO6TTz5RfHy8wsLCFBYWprVr12rt2rUKCwujHhZp3LixsrKydOXKFce0rKwsSVKzZs2oyU12/Phxx5Uw2by9vVWuXDleI8XJ4oGxN9Wjjz5qYmNjzfnz5x1Xx8yePdvqbpVKKSkppkOHDmbMmDHGbrc7zTtz5owJCwszS5YsMRkZGWbr1q0mNDTUbN261RhjHCPPt27d6hhp3rp1a3P27FkLtqR0Gj16tOPqGOphjYyMDNOlSxczbNgwk5aWZpKTk80TTzxhhg4dSk0ssH//fhMUFGQWLFhgMjMzzZEjR8z9999vpk6dSj2KkYcx/zcMuAxISkrSK6+8ori4OHl6eio6OlojR450GjyJorFkyRJNnTpVvr6+8vDwcJqXkJCgxMREvfrqq/r5559Vo0YNxcTEqHfv3o42n3zyiRYsWKCTJ0+qcePG+utf/6oWLVrc7M0otbLvETJ16lRJoh4WOXnypKZOnart27fr8uXL6tSpk8aPH68qVapQEwts2bJFM2fO1C+//KLKlSurZ8+ejiv8qEfxKFMhBAAAuI8yMyYEAAC4F0IIAACwBCEEAABYghACAAAsQQgBAACWIIQAAABLEEIAAIAlCCEAAMAShBAAAGAJQggAALAEIQQAAFji/wNsV5ZmfrK1dAAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Relative difference between Google and derived XEB fidelities\")\n",
    "plt.plot(depths, (fids[\"fidelity\"]-Fxeb)/Fxeb, 'o', label=\"XEB fidelity\")\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T03:45:23.116366Z",
     "start_time": "2024-02-27T03:45:23.049668Z"
    }
   },
   "id": "8899e621844a5f2",
   "execution_count": 17
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T03:45:23.117713Z",
     "start_time": "2024-02-27T03:45:23.116554Z"
    }
   },
   "id": "afe10a6dc8a144c7",
   "execution_count": 17
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-27T03:45:23.120082Z",
     "start_time": "2024-02-27T03:45:23.118496Z"
    }
   },
   "id": "aef54c572825dd04",
   "execution_count": 17
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}