research/old/isingexp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time dependence on random ising model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from qreservoirpy import QReservoir, Layers, utilities\n",
    "\n",
    "from qiskit.extensions import HamiltonianGate\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import RidgeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from tqdm import tqdm\n",
    "\n",
    "models = np.array([\n",
    "    SVC(kernel=ker) for ker in ['linear', 'poly', 'rbf', 'sigmoid']\n",
    "] + [\n",
    "    RidgeClassifier(alpha=alph) for alph in np.linspace(0, 1e-3, 20)\n",
    "] + [\n",
    "    KNeighborsClassifier(n_neighbors=n) for n in range(1, 10)\n",
    "], dtype=object)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "n_qubits = 8\n",
    "SHOTS = 1000\n",
    "timeseries = [0, 1, 2, 1, 2] * 10\n",
    "encoder = {\n",
    "    0: '00',\n",
    "    1: '01',\n",
    "    2: '10',\n",
    "    3: '11'\n",
    "}\n",
    "\n",
    "H = utilities.random_ising_H(num_qubits=n_qubits, num_terms=160)\n",
    "\n",
    "placement = {\n",
    "    0: [0, 1],\n",
    "    1: [1, 2],\n",
    "    2: [2, 3],\n",
    "}\n",
    "\n",
    "\n",
    "\n",
    "def build_method(circuit, timestep, encoding, reservoir, pos):\n",
    "        circuit.measure(range(4))\n",
    "        # circuit.reset(range(4))\n",
    "        circuit.initialize(encoding[timestep], pos[timestep])\n",
    "        # circuit.initialize(encoding[timestep], [0, 1])\n",
    "        # circuit.initialize(encoding[timestep], [2, 3])\n",
    "        circuit.unitary(reservoir, circuit.qubits)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EXPERIMENT: 100%|██████████| 50/50 [1:13:22<00:00, 88.06s/it] \n"
     ]
    }
   ],
   "source": [
    "\n",
    "experiment_results = []\n",
    "experiment_methods = []\n",
    "\n",
    "tvals = np.linspace(0, 2, 50)\n",
    "for exp_id in tqdm(range(len(tvals)), desc=\"EXPERIMENT\"):\n",
    "    T = tvals[exp_id]\n",
    "\n",
    "    UT = HamiltonianGate(H, T, label=\"UT\")\n",
    "\n",
    "    qres = QReservoir(qubits=n_qubits, layers=[\n",
    "        Layers.Reset(),\n",
    "        Layers.Timeseries(build_method=build_method,\n",
    "                        encoding = encoder,\n",
    "                        reservoir = UT,\n",
    "                        pos = placement)\n",
    "    ])\n",
    "\n",
    "    states = qres.run(\n",
    "        timeseries=timeseries,\n",
    "        shots=SHOTS,\n",
    "        disable_status_bar=True\n",
    "    )\n",
    "\n",
    "\n",
    "    WARMUP = 0.1 # 10%\n",
    "    warmup_idx = int(len(states) * WARMUP)\n",
    "\n",
    "    xstates = states[:-1][warmup_idx:]\n",
    "    target = timeseries[1:][warmup_idx:]\n",
    "\n",
    "    results = np.zeros(len(models))\n",
    "\n",
    "    N = 100\n",
    "    for _ in range(N):\n",
    "        X_train, X_test, y_train, y_test = train_test_split(xstates, target, test_size=1/3)\n",
    "        for i, model in enumerate(models):\n",
    "            model.fit(X_train, y_train)\n",
    "            score = model.score(X_test, y_test)\n",
    "            results[i] += score\n",
    "\n",
    "    results /= N\n",
    "\n",
    "    sorting = np.flip(np.argsort(results))\n",
    " \n",
    "    experiment_methods.append([models[sorting]])\n",
    "    experiment_results.append([results[sorting]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tikzplotlib\n",
    "\n",
    "res = np.array(experiment_results).reshape((len(tvals), -1))\n",
    "exp = np.array(experiment_methods).reshape((len(experiment_methods), -1))\n",
    "# plt.plot(tvals, res[:, 0])\n",
    "# print(exp[:, 0])\n",
    "cmap = plt.get_cmap('jet', 3)\n",
    "for t, r, m in zip(tvals, res[:, 0], exp[:, 0]):\n",
    "    col = -1\n",
    "\n",
    "    if isinstance(m, RidgeClassifier):\n",
    "        col = 0\n",
    "    elif isinstance(m, SVC):\n",
    "        col = 1\n",
    "    elif isinstance(m, KNeighborsClassifier):\n",
    "        col = 2\n",
    "        \n",
    "    plt.scatter(t, r, color=cmap(col))\n",
    "\n",
    "tikzplotlib.save(\"timedependentising.tex\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "quantum",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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