{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rotosolve optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lambeq import BobcatParser\n",
    "\n",
    "parser = BobcatParser()\n",
    "train_diagram = parser.sentence2diagram('Alice loves Bob')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create circuits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lambeq import StronglyEntanglingAnsatz, AtomicType\n",
    "\n",
    "ansatz = StronglyEntanglingAnsatz({AtomicType.NOUN: 1, AtomicType.SENTENCE: 1},\n",
    "                                   n_layers=2, n_single_qubit_params=3)\n",
    "\n",
    "train_circuit = ansatz(train_diagram)\n",
    "\n",
    "train_circuit.draw(figsize=(14, 12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameterise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lambeq import NumpyModel\n",
    "\n",
    "model = NumpyModel.from_diagrams([train_circuit], use_jit=True)\n",
    "model.initialise_weights()\n",
    "model([train_circuit]).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define evaluation metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array(-0.21474883, dtype=float32)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from jax import jit\n",
    "\n",
    "@jit\n",
    "def loss(y_pred, _):\n",
    "    \"\"\"The goal is to minimise the expectation value of the Pauli Z operator.\n",
    "    lambeq does not provide functionality to directly calculate expectation\n",
    "    values. Therefore, we need to calculate the expectation value from the\n",
    "    measurement probabilities of the Ket(0) and Ket(1) using their\n",
    "    eigenvalues.\"\"\"\n",
    "\n",
    "    # 0 state probability\n",
    "    p0 = y_pred[:, 0]\n",
    "    # 1 state probability\n",
    "    p1 = y_pred[:, 1]\n",
    "\n",
    "    # expectation value\n",
    "    exp = p0 - p1  # eigenvalues are 1 and -1\n",
    "\n",
    "    return - exp.mean()  # minimise expectation of measuring a 1\n",
    "\n",
    "loss(model([train_circuit]), None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lambeq import Dataset\n",
    "\n",
    "train_dataset = Dataset(\n",
    "            [train_circuit],\n",
    "            [-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [00:02<00:00,  8.96it/s]\n"
     ]
    }
   ],
   "source": [
    "from lambeq import QuantumTrainer\n",
    "from lambeq import RotosolveOptimizer\n",
    "from tqdm import trange\n",
    "\n",
    "EPOCHS = 5\n",
    "\n",
    "losses = []\n",
    "\n",
    "for i in trange(20):  # calculate results for 100 different seeds\n",
    "\n",
    "    trainer = QuantumTrainer(\n",
    "        model,\n",
    "        loss_function=loss,\n",
    "        epochs=EPOCHS,\n",
    "        optimizer=RotosolveOptimizer,\n",
    "        optim_hyperparams={},\n",
    "        evaluate_on_train=True,\n",
    "        verbose='suppress',\n",
    "        seed=i\n",
    "    )\n",
    "\n",
    "    initial_loss = loss(trainer.model([train_circuit]), None)\n",
    "\n",
    "    trainer.fit(train_dataset, log_interval=12)\n",
    "    losses.append([initial_loss] + trainer.train_epoch_costs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get mean and std of the losses\n",
    "import numpy as np\n",
    "\n",
    "mean_losses = np.mean(losses, axis=0)\n",
    "std_losses = np.std(losses, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "ax.set_title('Training set')\n",
    "ax.set_xlabel('Epochs')\n",
    "ax.set_ylabel('Loss')\n",
    "\n",
    "colours = iter(plt.rcParams['axes.prop_cycle'].by_key()['color'])\n",
    "ax.plot(mean_losses, color=next(colours));\n",
    "ax.fill_between(range(len(mean_losses)), mean_losses-std_losses, mean_losses+std_losses, alpha=0.2, color=next(colours));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([[1., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model([train_circuit]).round(3)"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}