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ScoomenstheMumensScoomenstheMumens
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added experiments
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‎examples/stabilizer/Unpickle logistic and henon map degree.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a3f4079e-8dab-4a6a-b326-53c84cde572c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"# import matplotlib.colors as mcolors\n",
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"import pickle\n",
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"import tikzplotlib\n",
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"from collections import defaultdict\n",
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"def tikzplotlib_fix_ncols(obj):\n",
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" \"\"\"\n",
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" workaround for matplotlib 3.6 renamed legend's _ncol to _ncols, which breaks tikzplotlib\n",
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" \"\"\"\n",
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" if hasattr(obj, \"_ncols\"):\n",
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" obj._ncol = obj._ncols\n",
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" for child in obj.get_children():\n",
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" tikzplotlib_fix_ncols(child)\n",
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"casename=\"henon\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "f95a6de7-6e84-43a0-86d6-d2bd23633036",
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"metadata": {},
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"outputs": [],
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"source": [
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"if casename==\"logistic\":\n",
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" from reservoirpy.datasets import logistic_map\n",
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" ts=logistic_map(100000, r=3.9, x0=0.5).flatten()\n",
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"elif casename==\"henon\":\n",
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" from run_reservoir import henon1d \n",
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" ts = henon1d(10000)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2cac7dd9",
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"metadata": {},
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"outputs": [],
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"source": [
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"num_qubits=6\n",
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"num_meas=3\n",
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"degree=2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4dec95e5-58ea-4932-8bc0-88e3a2285a25",
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_data(ep, num_qubits, num_meas, method, noise=\"None\", decode=True):\n",
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" degree=num_meas\n",
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" num_reservoirs=20\n",
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" timeplex=1\n",
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" string_identifier=str(ep)+\"_casename\"+str(casename)+\"_num_qubits\"+str(num_qubits)+\"_num_meas\"+str(num_meas)\n",
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" string_identifier+=\"_degree\"+str(degree)+\"_num_reservoirs\"+str(num_reservoirs)+\"_timeplex\"+str(timeplex)\n",
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" string_identifier+=\"_method\"+str(method)+\"_noise\"+str(noise)\n",
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" if not decode:\n",
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" string_identifier+=\"_decodeFalse\"\n",
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" string_identifier+=\".pickle\"\n",
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" name=\"X_train\"+string_identifier\n",
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" # print(\"name=\",name)\n",
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" with open(name,\"rb\") as f:\n",
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" X_train = pickle.load(f)\n",
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" \n",
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" name=\"X_test\"+string_identifier\n",
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" with open(name,\"rb\") as f:\n",
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" X_test = pickle.load(f)\n",
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" \n",
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" name=\"score\"+string_identifier\n",
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" with open(name,\"rb\") as f:\n",
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" score = pickle.load(f)\n",
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" \n",
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" name=\"prediction\"+string_identifier\n",
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" with open(name,\"rb\") as f:\n",
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" prediction = pickle.load(f)\n",
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" \n",
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" name=\"state\"+string_identifier\n",
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" with open(name,\"rb\") as f:\n",
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" state = pickle.load(f)\n",
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" return X_train, X_test, score, prediction, state, string_identifier"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "43ea3fa2-780c-4d55-b69b-7e1730bce1a0",
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"metadata": {},
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"outputs": [],
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"source": [
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"# X_train, X_test, score, prediction, state, basen = get_data(ep, 7, 6, \"classical\", \"None\")\n",
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"# _=plt.plot(state)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9fdee019",
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"metadata": {},
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"outputs": [],
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"source": [
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"for ep in [0,4]:\n",
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" X_train, X_test, score, prediction, states, basen = get_data(ep, num_qubits, num_meas, \"classical\", \"None\")\n",
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" data=states\n",
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" groups = defaultdict(list)\n",
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" for n, m, _ , stats in data:\n",
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" mean, var = stats\n",
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" x = n\n",
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" groups[n].append((x, float(mean), float(var)))\n",
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"\n",
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" # Plot each group\n",
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" plt.figure(figsize=(8,6))\n",
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"\n",
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" for n, values in groups.items():\n",
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" values.sort(key=lambda v: v[0]) # sort by x = n/m\n",
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" xs = [v[0] for v in values]\n",
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" ys = [v[1] for v in values]\n",
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" errs = [v[2] for v in values]\n",
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" plt.errorbar(xs, ys, yerr=errs, label=f\"n = {n}\", marker='o', capsize=4)\n",
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"\n",
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" plt.xlabel(\"degree\")\n",
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" plt.ylabel(\"Mean ± Variance\")\n",
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" plt.legend()\n",
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" plt.grid(True)\n",
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" plt.show()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎examples/stabilizer/Unpickle logistic and henon map.ipynb‎

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"print(nh)\n",
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"len(h), len(nh)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a9b6f0bf-beaf-4397-a2a4-59e0624a65a3",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

‎examples/stabilizer/createIsingParams.py‎

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with open("isingparams_"+"num_qubits"+str(num_qubits)+"_num_reservoirs"+str(num_reservoirs)+".pickle","wb") as f:
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pickle.dump(isingparams, f)
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createanddumpsising(3)
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createanddumpsising(4)
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createanddumpsising(5)
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for num_qubits in range (3,11):
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createanddumpsising(num_qubits)
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‎examples/stabilizer/createRandomTableaus.py‎

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from quantumreservoirpy.stabilizer import Stabilizer
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import pickle
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def createanddumptableaus(num_qubits, num_measurements):
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def createanddumptableaus(num_qubits, num_measurements,degree=None):
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num_tableaus = 100
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tableaus = {}
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for nr in range(1, num_tableaus+ 1):
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tableaus[nr] = Stabilizer.generate_tableau(num_qubits, num_measurements)
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with open("tableau_"+"num_qubits"+str(num_qubits)+"_num_measurements"+str(num_measurements)+"_num_tableaus"+str(num_tableaus)+".pickle","wb") as f:
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pickle.dump(tableaus, f)
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if degree is not None:
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tableaus[nr] = Stabilizer.generate_tableau(num_qubits, num_measurements,stab_method='degree', degree=degree)
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with open("tableau_"+"num_qubits"+str(num_qubits)+"_num_measurements"+str(num_measurements)+"degree"+str(degree)+"_num_tableaus"+str(num_tableaus)+".pickle","wb") as f:
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pickle.dump(tableaus, f)
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else:
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tableaus[nr] = Stabilizer.generate_tableau(num_qubits, num_measurements)
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with open("tableau_"+"num_qubits"+str(num_qubits)+"_num_measurements"+str(num_measurements)+"_num_tableaus"+str(num_tableaus)+".pickle","wb") as f:
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pickle.dump(tableaus, f)
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createanddumptableaus(5,3)
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createanddumptableaus(4,3)
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createanddumptableaus(3,2)
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for num_qubits in range (0,8):
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for num_meas in range (1,num_qubits):
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for deg in range (1,num_qubits):
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createanddumptableaus(num_qubits,num_meas,degree=deg)

‎examples/stabilizer/run_concentration.sh‎

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#!/bin/bash
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for name in {"henon","logistic"}
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do
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for i in {1..6}
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do
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sbatch run_reservoir_degree.sh 7 6 20 quantum_stab None 4 1 $name $i fixed_stab 1 1000
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done

‎examples/stabilizer/run_num_meas.sh‎

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#!/bin/bash
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for name in {"henon","logistic"}
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do
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for i in {1..7}
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do
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sbatch run_reservoir.sh 7 $i 20 classical None 4 1 $name 1 random 1 1000
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sbatch run_reservoir.sh 7 $i 20 quantum_part None 4 1 $name 1 random 1 1000
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sbatch run_reservoir.sh 7 $i 20 quantum_stab None 4 1 $name 1 random 1 1000
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done

‎examples/stabilizer/run_num_qubits.sh‎

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#!/bin/bash
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for name in {"henon","logistic"}
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do
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for i in {3..10}
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do
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sbatch run_reservoir.sh $i $(i-1) 20 classical None 4 1 $name 1 random 1 1000
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sbatch run_reservoir.sh $i $(i-1) 20 quantum_part None 4 1 $name 1 random 1 1000
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sbatch run_reservoir.sh $i $(i-1) 20 quantum_stab None 4 1 $name 1 random 1 1000
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done

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