STM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "38ddcf81",
   "metadata": {},
   "source": [
    "# Split Training Model For The Hybrid Autoencoder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81ce5067",
   "metadata": {},
   "source": [
    "### Library imports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fa04b7b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras import layers, losses\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1ef1378a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of modes\n",
    "modes = 1\n",
    "\n",
    "# tt\n",
    "train_state_select = 0\n",
    "\n",
    "# Cutoff dimension (number of Fock states)\n",
    "cutoff_dim = 3\n",
    "\n",
    "# Input vector length.\n",
    "input_len = 3\n",
    "\n",
    "# Number of layers (depth)\n",
    "Qlayers = 25\n",
    "\n",
    "# Number of steps in optimization routine performing gradient descent\n",
    "reps = 200\n",
    "\n",
    "# Learning rate\n",
    "lr = 0.05\n",
    "\n",
    "# Standard deviation of initial parameters\n",
    "passive_sd = 0.4\n",
    "active_sd = 0.02\n",
    "\n",
    "# The gamma parameter in the penalty function, given by the reference paper.\n",
    "norm_weight = 400\n",
    "\n",
    "# Seeds for the RNG functions to be able to reproduce results.\n",
    "tf.random.set_seed(137)\n",
    "np.random.seed(137)\n",
    "\n",
    "# Phase space circile restriciton radius.\n",
    "alpha_clip = 5\n",
    "\n",
    "save_folder_name = str(input_len) + '_inputs'\n",
    "\n",
    "os.makedirs(save_folder_name, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64b61dfa",
   "metadata": {},
   "source": [
    "### Initialization of the classical encoder and decoder networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7e0e5c62",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 2\n",
    "\n",
    "class Autoencoder(Model):\n",
    "  def __init__(self, latent_dim):\n",
    "    super(Autoencoder, self).__init__()\n",
    "    self.latent_dim = latent_dim   \n",
    "    self.encoder = tf.keras.Sequential([\n",
    "      layers.Dense(input_len, activation =\"elu\"),\n",
    "      layers.Dense(5, activation=\"elu\"),\n",
    "      layers.Dense(5, activation=\"elu\"),\n",
    "      layers.Dense(5, activation=\"elu\"),\n",
    "      layers.Dense(5, activation=\"elu\"),\n",
    "      layers.Dense(5, activation=\"elu\"),\n",
    "      layers.Dense(5, activation=\"elu\"),\n",
    "      layers.Dense(5, activation=\"elu\"),\n",
    "      layers.Dense(5, activation=\"elu\"),\n",
    "      layers.Dense(5, activation=\"elu\"),\n",
    "      layers.Dense(5, activation=\"elu\"),\n",
    "      layers.Dense(5, activation=\"elu\"),\n",
    "      layers.Dense(5, activation=\"elu\"),\n",
    "      layers.Dense(4, activation=\"elu\"),\n",
    "      layers.Dense(3, activation=\"elu\"),\n",
    "      layers.Dense(latent_dim, activation=\"elu\"),\n",
    "    ])\n",
    "    \n",
    "    self.decoder = tf.keras.Sequential([\n",
    "        layers.Dense(input_len, activation=\"elu\"),\n",
    "        layers.Dense(3, activation=\"elu\"),\n",
    "        layers.Dense(4, activation=\"elu\"),\n",
    "        layers.Dense(5, activation=\"elu\"),\n",
    "        layers.Dense(5, activation=\"elu\"),\n",
    "        layers.Dense(5, activation=\"elu\"),\n",
    "        layers.Dense(5, activation=\"elu\"),\n",
    "        layers.Dense(5, activation=\"elu\"),\n",
    "        layers.Dense(5, activation=\"elu\"),\n",
    "        layers.Dense(5, activation=\"elu\"),\n",
    "        layers.Dense(5, activation=\"elu\"),\n",
    "        layers.Dense(5, activation=\"elu\"),\n",
    "        layers.Dense(5, activation=\"elu\"),\n",
    "        layers.Dense(4, activation=\"elu\"),\n",
    "        layers.Dense(3, activation=\"elu\"),\n",
    "        layers.Dense(input_len, activation=\"elu\")\n",
    "    ])\n",
    "    \n",
    "\n",
    "  def call(self, x):\n",
    "    encoded = self.encoder(x)\n",
    "    decoded = self.decoder(encoded)\n",
    "    return decoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cdf05bf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "autoencoder = Autoencoder(latent_dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "93fe5ac2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The target state for the training is chosen to be [1. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "def GenerateTargetState(input_len, f):\n",
    "    state = np.zeros(input_len)\n",
    "    state[f] = 1.0\n",
    "    train = np.array([state])\n",
    "    return train, state\n",
    "    \n",
    "    \n",
    "x_train, target_state = GenerateTargetState(input_len, train_state_select)\n",
    "print('The target state for the training is chosen to be ' + str(target_state))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e38e0970",
   "metadata": {},
   "source": [
    "### Compile the classical autoencoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "46699d99",
   "metadata": {},
   "outputs": [],
   "source": [
    "autoencoder.compile(optimizer='adam', loss=losses.MeanSquaredError())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "264a9361",
   "metadata": {},
   "source": [
    "### Train the classical autoencoder and obtain the latent space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "41045d77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "1/1 [==============================] - 1s 1s/step - loss: 0.3366 - val_loss: 0.3246\n",
      "Epoch 2/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.3246 - val_loss: 0.3141\n",
      "Epoch 3/300\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.3141 - val_loss: 0.3042\n",
      "Epoch 4/300\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.3042 - val_loss: 0.2947\n",
      "Epoch 5/300\n",
      "1/1 [==============================] - 0s 18ms/step - loss: 0.2947 - val_loss: 0.2854\n",
      "Epoch 6/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.2854 - val_loss: 0.2764\n",
      "Epoch 7/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.2764 - val_loss: 0.2676\n",
      "Epoch 8/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.2676 - val_loss: 0.2590\n",
      "Epoch 9/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.2590 - val_loss: 0.2508\n",
      "Epoch 10/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.2508 - val_loss: 0.2427\n",
      "Epoch 11/300\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.2427 - val_loss: 0.2349\n",
      "Epoch 12/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.2349 - val_loss: 0.2274\n",
      "Epoch 13/300\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.2274 - val_loss: 0.2202\n",
      "Epoch 14/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.2202 - val_loss: 0.2133\n",
      "Epoch 15/300\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.2133 - val_loss: 0.2067\n",
      "Epoch 16/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.2067 - val_loss: 0.2004\n",
      "Epoch 17/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.2004 - val_loss: 0.1944\n",
      "Epoch 18/300\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.1944 - val_loss: 0.1888\n",
      "Epoch 19/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.1888 - val_loss: 0.1835\n",
      "Epoch 20/300\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.1835 - val_loss: 0.1785\n",
      "Epoch 21/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.1785 - val_loss: 0.1738\n",
      "Epoch 22/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.1738 - val_loss: 0.1695\n",
      "Epoch 23/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.1695 - val_loss: 0.1654\n",
      "Epoch 24/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.1654 - val_loss: 0.1616\n",
      "Epoch 25/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.1616 - val_loss: 0.1580\n",
      "Epoch 26/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.1580 - val_loss: 0.1546\n",
      "Epoch 27/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.1546 - val_loss: 0.1514\n",
      "Epoch 28/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.1514 - val_loss: 0.1483\n",
      "Epoch 29/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.1483 - val_loss: 0.1454\n",
      "Epoch 30/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.1454 - val_loss: 0.1426\n",
      "Epoch 31/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.1426 - val_loss: 0.1399\n",
      "Epoch 32/300\n",
      "1/1 [==============================] - 0s 18ms/step - loss: 0.1399 - val_loss: 0.1372\n",
      "Epoch 33/300\n",
      "1/1 [==============================] - 0s 18ms/step - loss: 0.1372 - val_loss: 0.1347\n",
      "Epoch 34/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.1347 - val_loss: 0.1322\n",
      "Epoch 35/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.1322 - val_loss: 0.1298\n",
      "Epoch 36/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.1298 - val_loss: 0.1275\n",
      "Epoch 37/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.1275 - val_loss: 0.1253\n",
      "Epoch 38/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.1253 - val_loss: 0.1232\n",
      "Epoch 39/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.1232 - val_loss: 0.1212\n",
      "Epoch 40/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.1212 - val_loss: 0.1192\n",
      "Epoch 41/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.1192 - val_loss: 0.1174\n",
      "Epoch 42/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.1174 - val_loss: 0.1157\n",
      "Epoch 43/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.1157 - val_loss: 0.1141\n",
      "Epoch 44/300\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.1141 - val_loss: 0.1125\n",
      "Epoch 45/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.1125 - val_loss: 0.1110\n",
      "Epoch 46/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.1110 - val_loss: 0.1095\n",
      "Epoch 47/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.1095 - val_loss: 0.1081\n",
      "Epoch 48/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.1081 - val_loss: 0.1068\n",
      "Epoch 49/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.1068 - val_loss: 0.1054\n",
      "Epoch 50/300\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.1054 - val_loss: 0.1041\n",
      "Epoch 51/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.1041 - val_loss: 0.1027\n",
      "Epoch 52/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.1027 - val_loss: 0.1014\n",
      "Epoch 53/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.1014 - val_loss: 0.1001\n",
      "Epoch 54/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.1001 - val_loss: 0.0989\n",
      "Epoch 55/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.0989 - val_loss: 0.0976\n",
      "Epoch 56/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.0976 - val_loss: 0.0963\n",
      "Epoch 57/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.0963 - val_loss: 0.0951\n",
      "Epoch 58/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.0951 - val_loss: 0.0939\n",
      "Epoch 59/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.0939 - val_loss: 0.0927\n",
      "Epoch 60/300\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.0927 - val_loss: 0.0915\n",
      "Epoch 61/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.0915 - val_loss: 0.0904\n",
      "Epoch 62/300\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.0904 - val_loss: 0.0892\n",
      "Epoch 63/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.0892 - val_loss: 0.0881\n",
      "Epoch 64/300\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.0881 - val_loss: 0.0870\n",
      "Epoch 65/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.0870 - val_loss: 0.0859\n",
      "Epoch 66/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.0859 - val_loss: 0.0848\n",
      "Epoch 67/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.0848 - val_loss: 0.0838\n",
      "Epoch 68/300\n",
      "1/1 [==============================] - 0s 22ms/step - loss: 0.0838 - val_loss: 0.0827\n",
      "Epoch 69/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.0827 - val_loss: 0.0817\n",
      "Epoch 70/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.0817 - val_loss: 0.0807\n",
      "Epoch 71/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.0807 - val_loss: 0.0797\n",
      "Epoch 72/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.0797 - val_loss: 0.0787\n",
      "Epoch 73/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.0787 - val_loss: 0.0778\n",
      "Epoch 74/300\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.0778 - val_loss: 0.0768\n",
      "Epoch 75/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.0768 - val_loss: 0.0759\n",
      "Epoch 76/300\n",
      "1/1 [==============================] - 0s 19ms/step - loss: 0.0759 - val_loss: 0.0749\n",
      "Epoch 77/300\n",
      "1/1 [==============================] - 0s 18ms/step - loss: 0.0749 - val_loss: 0.0740\n",
      "Epoch 78/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.0740 - val_loss: 0.0731\n",
      "Epoch 79/300\n",
      "1/1 [==============================] - 0s 18ms/step - loss: 0.0731 - val_loss: 0.0722\n",
      "Epoch 80/300\n",
      "1/1 [==============================] - 0s 20ms/step - loss: 0.0722 - val_loss: 0.0713\n",
      "Epoch 81/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.0713 - val_loss: 0.0704\n",
      "Epoch 82/300\n",
      "1/1 [==============================] - 0s 18ms/step - loss: 0.0704 - val_loss: 0.0696\n",
      "Epoch 83/300\n",
      "1/1 [==============================] - 0s 32ms/step - loss: 0.0696 - val_loss: 0.0687\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 84/300\n",
      "1/1 [==============================] - 0s 49ms/step - loss: 0.0687 - val_loss: 0.0678\n",
      "Epoch 85/300\n",
      "1/1 [==============================] - 0s 39ms/step - loss: 0.0678 - val_loss: 0.0670\n",
      "Epoch 86/300\n",
      "1/1 [==============================] - 0s 28ms/step - loss: 0.0670 - val_loss: 0.0661\n",
      "Epoch 87/300\n",
      "1/1 [==============================] - 0s 28ms/step - loss: 0.0661 - val_loss: 0.0653\n",
      "Epoch 88/300\n",
      "1/1 [==============================] - 0s 36ms/step - loss: 0.0653 - val_loss: 0.0644\n",
      "Epoch 89/300\n",
      "1/1 [==============================] - 0s 30ms/step - loss: 0.0644 - val_loss: 0.0636\n",
      "Epoch 90/300\n",
      "1/1 [==============================] - 0s 23ms/step - loss: 0.0636 - val_loss: 0.0628\n",
      "Epoch 91/300\n",
      "1/1 [==============================] - 0s 24ms/step - loss: 0.0628 - val_loss: 0.0620\n",
      "Epoch 92/300\n",
      "1/1 [==============================] - 0s 25ms/step - loss: 0.0620 - val_loss: 0.0612\n",
      "Epoch 93/300\n",
      "1/1 [==============================] - 0s 21ms/step - loss: 0.0612 - val_loss: 0.0604\n",
      "Epoch 94/300\n",
      "1/1 [==============================] - 0s 19ms/step - loss: 0.0604 - val_loss: 0.0596\n",
      "Epoch 95/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.0596 - val_loss: 0.0588\n",
      "Epoch 96/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.0588 - val_loss: 0.0580\n",
      "Epoch 97/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.0580 - val_loss: 0.0572\n",
      "Epoch 98/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.0572 - val_loss: 0.0564\n",
      "Epoch 99/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.0564 - val_loss: 0.0557\n",
      "Epoch 100/300\n",
      "1/1 [==============================] - 0s 18ms/step - loss: 0.0557 - val_loss: 0.0549\n",
      "Epoch 101/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.0549 - val_loss: 0.0541\n",
      "Epoch 102/300\n",
      "1/1 [==============================] - 0s 18ms/step - loss: 0.0541 - val_loss: 0.0534\n",
      "Epoch 103/300\n",
      "1/1 [==============================] - 0s 18ms/step - loss: 0.0534 - val_loss: 0.0526\n",
      "Epoch 104/300\n",
      "1/1 [==============================] - 0s 18ms/step - loss: 0.0526 - val_loss: 0.0519\n",
      "Epoch 105/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.0519 - val_loss: 0.0511\n",
      "Epoch 106/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.0511 - val_loss: 0.0504\n",
      "Epoch 107/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.0504 - val_loss: 0.0497\n",
      "Epoch 108/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.0497 - val_loss: 0.0489\n",
      "Epoch 109/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.0489 - val_loss: 0.0482\n",
      "Epoch 110/300\n",
      "1/1 [==============================] - 0s 18ms/step - loss: 0.0482 - val_loss: 0.0475\n",
      "Epoch 111/300\n",
      "1/1 [==============================] - 0s 18ms/step - loss: 0.0475 - val_loss: 0.0468\n",
      "Epoch 112/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.0468 - val_loss: 0.0461\n",
      "Epoch 113/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.0461 - val_loss: 0.0454\n",
      "Epoch 114/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.0454 - val_loss: 0.0446\n",
      "Epoch 115/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.0446 - val_loss: 0.0439\n",
      "Epoch 116/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.0439 - val_loss: 0.0432\n",
      "Epoch 117/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.0432 - val_loss: 0.0426\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 246/300\n",
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      "1/1 [==============================] - 0s 17ms/step - loss: 2.9500e-08 - val_loss: 5.0638e-08\n",
      "Epoch 279/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 5.0638e-08 - val_loss: 7.4058e-08\n",
      "Epoch 280/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 7.4058e-08 - val_loss: 9.8265e-08\n",
      "Epoch 281/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 9.8265e-08 - val_loss: 1.2210e-07\n",
      "Epoch 282/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 1.2210e-07 - val_loss: 1.4466e-07\n",
      "Epoch 283/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 1.4466e-07 - val_loss: 1.6533e-07\n",
      "Epoch 284/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 1.6533e-07 - val_loss: 1.8366e-07\n",
      "Epoch 285/300\n",
      "1/1 [==============================] - 0s 18ms/step - loss: 1.8366e-07 - val_loss: 1.9934e-07\n",
      "Epoch 286/300\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 1.9934e-07 - val_loss: 2.1231e-07\n",
      "Epoch 287/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 2.1231e-07 - val_loss: 2.2245e-07\n",
      "Epoch 288/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 2.2245e-07 - val_loss: 2.2988e-07\n",
      "Epoch 289/300\n",
      "1/1 [==============================] - 0s 18ms/step - loss: 2.2988e-07 - val_loss: 2.3463e-07\n",
      "Epoch 290/300\n",
      "1/1 [==============================] - 0s 18ms/step - loss: 2.3463e-07 - val_loss: 2.3695e-07\n",
      "Epoch 291/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 2.3695e-07 - val_loss: 2.3698e-07\n",
      "Epoch 292/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 2.3698e-07 - val_loss: 2.3495e-07\n",
      "Epoch 293/300\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 2.3495e-07 - val_loss: 2.3109e-07\n",
      "Epoch 294/300\n",
      "1/1 [==============================] - 0s 18ms/step - loss: 2.3109e-07 - val_loss: 2.2565e-07\n",
      "Epoch 295/300\n",
      "1/1 [==============================] - 0s 18ms/step - loss: 2.2565e-07 - val_loss: 2.1884e-07\n",
      "Epoch 296/300\n",
      "1/1 [==============================] - 0s 18ms/step - loss: 2.1884e-07 - val_loss: 2.1094e-07\n",
      "Epoch 297/300\n",
      "1/1 [==============================] - 0s 18ms/step - loss: 2.1094e-07 - val_loss: 2.0207e-07\n",
      "Epoch 298/300\n",
      "1/1 [==============================] - 0s 18ms/step - loss: 2.0207e-07 - val_loss: 1.9253e-07\n",
      "Epoch 299/300\n",
      "1/1 [==============================] - 0s 19ms/step - loss: 1.9253e-07 - val_loss: 1.8247e-07\n",
      "Epoch 300/300\n",
      "1/1 [==============================] - 0s 18ms/step - loss: 1.8247e-07 - val_loss: 1.7205e-07\n"
     ]
    }
   ],
   "source": [
    "history = autoencoder.fit(x_train, x_train,\n",
    "                epochs=300,\n",
    "                validation_data=(x_train, x_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1662965c",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoded_st = autoencoder.encoder(x_train).numpy()\n",
    "decoded_st = autoencoder.decoder(encoded_st).numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "425d0977",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1fb4ed59070>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history[\"loss\"], label=\"Training Loss\")\n",
    "plt.plot(history.history[\"val_loss\"], label=\"Validation Loss\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "63151609",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.5461941 -0.7412164]]\n"
     ]
    }
   ],
   "source": [
    "print(encoded_st)\n",
    "np.savetxt(save_folder_name +'/encoded_ '+ str(train_state_select) +'.txt', encoded_st)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1511dead",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Strawberry Fields: a Python library for continuous-variable quantum circuits.\n",
      "Copyright 2018-2020 Xanadu Quantum Technologies Inc.\n",
      "\n",
      "Python version:            3.9.7\n",
      "Platform info:             Windows-10-10.0.22000-SP0\n",
      "Installation path:         C:\\Users\\Bartu\\anaconda3\\envs\\qml\\lib\\site-packages\\strawberryfields\n",
      "Strawberry Fields version: 0.23.0\n",
      "Numpy version:             1.22.2\n",
      "Scipy version:             1.8.0\n",
      "SymPy version:             1.9\n",
      "NetworkX version:          2.6.3\n",
      "The Walrus version:        0.18.0\n",
      "Blackbird version:         0.4.0\n",
      "XCC version:               0.2.1\n",
      "TensorFlow version:        2.8.0\n"
     ]
    }
   ],
   "source": [
    "import strawberryfields as sf\n",
    "from strawberryfields import ops\n",
    "sf.about()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d8d06c7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def layer(params, q):\n",
    "    \"\"\"CV quantum neural network layer acting on ``N`` modes.\n",
    "\n",
    "    Args:\n",
    "        params (list[float]): list of length ``2*(max(1, N-1) + N**2 + n)`` containing\n",
    "            the number of parameters for the layer\n",
    "        q (list[RegRef]): list of Strawberry Fields quantum registers the layer\n",
    "            is to be applied to\n",
    "    \"\"\"\n",
    "    ops.Dgate(tf.clip_by_value(encoded_st[0][0], clip_value_min = -alpha_clip, clip_value_max = alpha_clip), math.degrees(encoded_st[0][1])) | q[0]\n",
    "    \n",
    "    N = len(q)\n",
    "    M = int(N * (N - 1)) + max(1, N - 1)\n",
    "\n",
    "    rphi = params[-N+1:]\n",
    "    s = params[M:M+N]\n",
    "    dr = params[2*M+N:2*M+2*N]\n",
    "    dp = params[2*M+2*N:2*M+3*N]\n",
    "    k = params[2*M+3*N:2*M+4*N]\n",
    "    \n",
    "    ops.Rgate(rphi[0]) | q[0]\n",
    "    \n",
    "    for i in range(N):\n",
    "        ops.Sgate(s[i]) | q[i]\n",
    "\n",
    "    ops.Rgate(rphi[0]) | q[0]\n",
    "\n",
    "    for i in range(N):\n",
    "        ops.Dgate(dr[i], dp[i]) | q[i]\n",
    "        ops.Kgate(k[i]) | q[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7c55a18",
   "metadata": {},
   "source": [
    "### Initialization of layer weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "50ffe5bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_weights(modes, layers, active_sd=0.0001, passive_sd=0.1):\n",
    "    \"\"\"Initialize a 2D TensorFlow Variable containing normally-distributed\n",
    "    random weights for an ``N`` mode quantum neural network with ``L`` layers.\n",
    "\n",
    "    Args:\n",
    "        modes (int): the number of modes in the quantum neural network\n",
    "        layers (int): the number of layers in the quantum neural network\n",
    "        active_sd (float): the standard deviation used when initializing\n",
    "            the normally-distributed weights for the active parameters\n",
    "            (displacement, squeezing, and Kerr magnitude)\n",
    "        passive_sd (float): the standard deviation used when initializing\n",
    "            the normally-distributed weights for the passive parameters\n",
    "            (beamsplitter angles and all gate phases)\n",
    "\n",
    "    Returns:\n",
    "        tf.Variable[tf.float32]: A TensorFlow Variable of shape\n",
    "        ``[layers, 2*(max(1, modes-1) + modes**2 + modes)]``, where the Lth\n",
    "        row represents the layer parameters for the Lth layer.\n",
    "    \"\"\"\n",
    "    # Number of interferometer parameters:\n",
    "    M = int(modes * (modes - 1)) + max(1, modes - 1)\n",
    "\n",
    "    # Create the TensorFlow variables\n",
    "    int1_weights = tf.random.normal(shape=[layers, M], stddev=passive_sd)\n",
    "    s_weights = tf.random.normal(shape=[layers, modes], stddev=active_sd)\n",
    "    int2_weights = tf.random.normal(shape=[layers, M], stddev=passive_sd)\n",
    "    dr_weights = tf.random.normal(shape=[layers, modes], stddev=active_sd)\n",
    "    dp_weights = tf.random.normal(shape=[layers, modes], stddev=passive_sd)\n",
    "    k_weights = tf.random.normal(shape=[layers, modes], stddev=active_sd)\n",
    "\n",
    "    weights = tf.concat(\n",
    "        [int1_weights, s_weights, int2_weights, dr_weights, dp_weights, k_weights], axis=1\n",
    "    )\n",
    "\n",
    "    weights = tf.Variable(weights)\n",
    "\n",
    "    return weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ff3c2e83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize engine and program\n",
    "eng = sf.Engine(backend=\"tf\", backend_options={\"cutoff_dim\": cutoff_dim})\n",
    "qnn = sf.Program(modes)\n",
    "\n",
    "# initialize QNN weights\n",
    "weights = init_weights(modes, Qlayers) # our TensorFlow weights\n",
    "num_params = np.prod(weights.shape)   # total number of parameters in our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8e592045",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bartu\\AppData\\Local\\Temp\\ipykernel_1808\\2132937993.py:3: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n"
     ]
    }
   ],
   "source": [
    "# Create array of Strawberry Fields symbolic gate arguments, matching\n",
    "# the size of the weights Variable.\n",
    "sf_params = np.arange(num_params).reshape(weights.shape).astype(np.str)\n",
    "sf_params = np.array([qnn.params(*i) for i in sf_params])\n",
    "\n",
    "\n",
    "# Construct the symbolic Strawberry Fields program by\n",
    "# looping and applying layers to the program.\n",
    "with qnn.context as q:\n",
    "    for k in range(Qlayers):\n",
    "        layer(sf_params[k], q)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebac1320",
   "metadata": {},
   "source": [
    "### Definition of the quantum cost function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4316ab44",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cost(weights):\n",
    "    # Create a dictionary mapping from the names of the Strawberry Fields\n",
    "    # free parameters to the TensorFlow weight values.\n",
    "    mapping = {p.name: w for p, w in zip(sf_params.flatten(), tf.reshape(weights, [-1]))}\n",
    "\n",
    "    # Run engine\n",
    "    state = eng.run(qnn, args=mapping).state\n",
    "\n",
    "    # Extract the statevector\n",
    "    ket = state.ket()\n",
    "\n",
    "    # Compute the fidelity between the output statevector\n",
    "    # and the target state.\n",
    "    fidelity = tf.abs(tf.reduce_sum(tf.math.conj(ket) * target_state)) ** 2\n",
    "\n",
    "    # Objective function to minimize\n",
    "    #cost = tf.abs(tf.reduce_sum(tf.math.conj(ket) * target_state) - 1)\n",
    "    #return cost, fidelity, ket\n",
    "    # Instead of the Cost function, maybe it is better to break it down to components\n",
    "    # at least, when the Fock basis is insufficent, it will be visible\n",
    "    difference = tf.reduce_sum(tf.abs(ket - target_state))\n",
    "    fidelity = tf.abs(tf.reduce_sum(tf.math.conj(ket) * target_state)) ** 2\n",
    "    return difference, fidelity, ket, tf.math.real(state.trace())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5af0d34",
   "metadata": {},
   "source": [
    "### Optimizer definition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a5afdd2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = tf.keras.optimizers.Adam(learning_rate=lr)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea3e4093",
   "metadata": {},
   "source": [
    "### Train the quantum decoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "99dc6da2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rep: 0 Cost: 1.0279 Fidelity: 0.0001 Trace: 0.0005\n",
      "Rep: 1 Cost: 1.0514 Fidelity: 0.0004 Trace: 0.0012\n",
      "Rep: 2 Cost: 1.0219 Fidelity: 0.0001 Trace: 0.0004\n",
      "Rep: 3 Cost: 1.0372 Fidelity: 0.0001 Trace: 0.0005\n",
      "Rep: 4 Cost: 1.0064 Fidelity: 0.0001 Trace: 0.0001\n",
      "Rep: 5 Cost: 0.9990 Fidelity: 0.0003 Trace: 0.0004\n",
      "Rep: 6 Cost: 0.9974 Fidelity: 0.0003 Trace: 0.0005\n",
      "Rep: 7 Cost: 0.9960 Fidelity: 0.0004 Trace: 0.0005\n",
      "Rep: 8 Cost: 0.9901 Fidelity: 0.0004 Trace: 0.0004\n",
      "Rep: 9 Cost: 0.9903 Fidelity: 0.0003 Trace: 0.0003\n",
      "Rep: 10 Cost: 0.9873 Fidelity: 0.0002 Trace: 0.0002\n",
      "Rep: 11 Cost: 0.9886 Fidelity: 0.0003 Trace: 0.0003\n",
      "Rep: 12 Cost: 0.9839 Fidelity: 0.0005 Trace: 0.0005\n",
      "Rep: 13 Cost: 0.9729 Fidelity: 0.0011 Trace: 0.0011\n",
      "Rep: 14 Cost: 0.9571 Fidelity: 0.0024 Trace: 0.0024\n",
      "Rep: 15 Cost: 0.9366 Fidelity: 0.0047 Trace: 0.0047\n",
      "Rep: 16 Cost: 0.9314 Fidelity: 0.0080 Trace: 0.0081\n",
      "Rep: 17 Cost: 0.8973 Fidelity: 0.0119 Trace: 0.0120\n",
      "Rep: 18 Cost: 0.8826 Fidelity: 0.0217 Trace: 0.0221\n",
      "Rep: 19 Cost: 0.8334 Fidelity: 0.0322 Trace: 0.0322\n",
      "Rep: 20 Cost: 0.8591 Fidelity: 0.0336 Trace: 0.0346\n",
      "Rep: 21 Cost: 0.7904 Fidelity: 0.0515 Trace: 0.0517\n",
      "Rep: 22 Cost: 0.8240 Fidelity: 0.0698 Trace: 0.0716\n",
      "Rep: 23 Cost: 0.7206 Fidelity: 0.0881 Trace: 0.0882\n",
      "Rep: 24 Cost: 0.8321 Fidelity: 0.0709 Trace: 0.0758\n",
      "Rep: 25 Cost: 0.8158 Fidelity: 0.0800 Trace: 0.0844\n",
      "Rep: 26 Cost: 0.6833 Fidelity: 0.1156 Trace: 0.1159\n",
      "Rep: 27 Cost: 0.7972 Fidelity: 0.1147 Trace: 0.1241\n",
      "Rep: 28 Cost: 0.8092 Fidelity: 0.1142 Trace: 0.1252\n",
      "Rep: 29 Cost: 0.6966 Fidelity: 0.1363 Trace: 0.1387\n",
      "Rep: 30 Cost: 0.7016 Fidelity: 0.1383 Trace: 0.1409\n",
      "Rep: 31 Cost: 0.7442 Fidelity: 0.1334 Trace: 0.1383\n",
      "Rep: 32 Cost: 0.6818 Fidelity: 0.1505 Trace: 0.1529\n",
      "Rep: 33 Cost: 0.6370 Fidelity: 0.1734 Trace: 0.1740\n",
      "Rep: 34 Cost: 0.6559 Fidelity: 0.1775 Trace: 0.1805\n",
      "Rep: 35 Cost: 0.6366 Fidelity: 0.1827 Trace: 0.1847\n",
      "Rep: 36 Cost: 0.5940 Fidelity: 0.2011 Trace: 0.2022\n",
      "Rep: 37 Cost: 0.5732 Fidelity: 0.2344 Trace: 0.2360\n",
      "Rep: 38 Cost: 0.5363 Fidelity: 0.2590 Trace: 0.2599\n",
      "Rep: 39 Cost: 0.5277 Fidelity: 0.2848 Trace: 0.2866\n",
      "Rep: 40 Cost: 0.4680 Fidelity: 0.3363 Trace: 0.3372\n",
      "Rep: 41 Cost: 0.4318 Fidelity: 0.3875 Trace: 0.3894\n",
      "Rep: 42 Cost: 0.4169 Fidelity: 0.4403 Trace: 0.4448\n",
      "Rep: 43 Cost: 0.3629 Fidelity: 0.4973 Trace: 0.5002\n",
      "Rep: 44 Cost: 0.3576 Fidelity: 0.5389 Trace: 0.5412\n",
      "Rep: 45 Cost: 0.3902 Fidelity: 0.5380 Trace: 0.5393\n",
      "Rep: 46 Cost: 0.4018 Fidelity: 0.5772 Trace: 0.5797\n",
      "Rep: 47 Cost: 0.2590 Fidelity: 0.6000 Trace: 0.6005\n",
      "Rep: 48 Cost: 0.4069 Fidelity: 0.5827 Trace: 0.5878\n",
      "Rep: 49 Cost: 0.3223 Fidelity: 0.6245 Trace: 0.6271\n",
      "Rep: 50 Cost: 0.3708 Fidelity: 0.6427 Trace: 0.6609\n",
      "Rep: 51 Cost: 0.3475 Fidelity: 0.6782 Trace: 0.6969\n",
      "Rep: 52 Cost: 0.2661 Fidelity: 0.6802 Trace: 0.6825\n",
      "Rep: 53 Cost: 0.3606 Fidelity: 0.6604 Trace: 0.6687\n",
      "Rep: 54 Cost: 0.2994 Fidelity: 0.7295 Trace: 0.7397\n",
      "Rep: 55 Cost: 0.2445 Fidelity: 0.7128 Trace: 0.7164\n",
      "Rep: 56 Cost: 0.2816 Fidelity: 0.6895 Trace: 0.6946\n",
      "Rep: 57 Cost: 0.2413 Fidelity: 0.7269 Trace: 0.7315\n",
      "Rep: 58 Cost: 0.1930 Fidelity: 0.7335 Trace: 0.7347\n",
      "Rep: 59 Cost: 0.2441 Fidelity: 0.7075 Trace: 0.7107\n",
      "Rep: 60 Cost: 0.2347 Fidelity: 0.7557 Trace: 0.7569\n",
      "Rep: 61 Cost: 0.2294 Fidelity: 0.7293 Trace: 0.7308\n",
      "Rep: 62 Cost: 0.1784 Fidelity: 0.7590 Trace: 0.7604\n",
      "Rep: 63 Cost: 0.2021 Fidelity: 0.7606 Trace: 0.7637\n",
      "Rep: 64 Cost: 0.1693 Fidelity: 0.7454 Trace: 0.7456\n",
      "Rep: 65 Cost: 0.1940 Fidelity: 0.7710 Trace: 0.7718\n",
      "Rep: 66 Cost: 0.2347 Fidelity: 0.7676 Trace: 0.7694\n",
      "Rep: 67 Cost: 0.2077 Fidelity: 0.8059 Trace: 0.8072\n",
      "Rep: 68 Cost: 0.1691 Fidelity: 0.7902 Trace: 0.7909\n",
      "Rep: 69 Cost: 0.1567 Fidelity: 0.8069 Trace: 0.8081\n",
      "Rep: 70 Cost: 0.1657 Fidelity: 0.7985 Trace: 0.8000\n",
      "Rep: 71 Cost: 0.1944 Fidelity: 0.8225 Trace: 0.8237\n",
      "Rep: 72 Cost: 0.1991 Fidelity: 0.8057 Trace: 0.8063\n",
      "Rep: 73 Cost: 0.1554 Fidelity: 0.8166 Trace: 0.8175\n",
      "Rep: 74 Cost: 0.1249 Fidelity: 0.8236 Trace: 0.8242\n",
      "Rep: 75 Cost: 0.1490 Fidelity: 0.8219 Trace: 0.8236\n",
      "Rep: 76 Cost: 0.1262 Fidelity: 0.8347 Trace: 0.8350\n",
      "Rep: 77 Cost: 0.2325 Fidelity: 0.8204 Trace: 0.8248\n",
      "Rep: 78 Cost: 0.1686 Fidelity: 0.8439 Trace: 0.8449\n",
      "Rep: 79 Cost: 0.1814 Fidelity: 0.8196 Trace: 0.8236\n",
      "Rep: 80 Cost: 0.1593 Fidelity: 0.8313 Trace: 0.8338\n",
      "Rep: 81 Cost: 0.2054 Fidelity: 0.8303 Trace: 0.8328\n",
      "Rep: 82 Cost: 0.1916 Fidelity: 0.8202 Trace: 0.8229\n",
      "Rep: 83 Cost: 0.1449 Fidelity: 0.8469 Trace: 0.8488\n",
      "Rep: 84 Cost: 0.1446 Fidelity: 0.8415 Trace: 0.8431\n",
      "Rep: 85 Cost: 0.1552 Fidelity: 0.8547 Trace: 0.8561\n",
      "Rep: 86 Cost: 0.1539 Fidelity: 0.8457 Trace: 0.8469\n",
      "Rep: 87 Cost: 0.1917 Fidelity: 0.8436 Trace: 0.8464\n",
      "Rep: 88 Cost: 0.1582 Fidelity: 0.8368 Trace: 0.8388\n",
      "Rep: 89 Cost: 0.1214 Fidelity: 0.8564 Trace: 0.8578\n",
      "Rep: 90 Cost: 0.1201 Fidelity: 0.8588 Trace: 0.8600\n",
      "Rep: 91 Cost: 0.1511 Fidelity: 0.8502 Trace: 0.8517\n",
      "Rep: 92 Cost: 0.1760 Fidelity: 0.8646 Trace: 0.8664\n",
      "Rep: 93 Cost: 0.1483 Fidelity: 0.8507 Trace: 0.8519\n",
      "Rep: 94 Cost: 0.1249 Fidelity: 0.8605 Trace: 0.8610\n",
      "Rep: 95 Cost: 0.1523 Fidelity: 0.8566 Trace: 0.8591\n",
      "Rep: 96 Cost: 0.1500 Fidelity: 0.8638 Trace: 0.8662\n",
      "Rep: 97 Cost: 0.1409 Fidelity: 0.8595 Trace: 0.8602\n",
      "Rep: 98 Cost: 0.1513 Fidelity: 0.8722 Trace: 0.8728\n",
      "Rep: 99 Cost: 0.1175 Fidelity: 0.8627 Trace: 0.8637\n",
      "Rep: 100 Cost: 0.1020 Fidelity: 0.8655 Trace: 0.8660\n",
      "Rep: 101 Cost: 0.1798 Fidelity: 0.8712 Trace: 0.8738\n",
      "Rep: 102 Cost: 0.1683 Fidelity: 0.8611 Trace: 0.8641\n",
      "Rep: 103 Cost: 0.1093 Fidelity: 0.8737 Trace: 0.8741\n",
      "Rep: 104 Cost: 0.1198 Fidelity: 0.8676 Trace: 0.8680\n",
      "Rep: 105 Cost: 0.1310 Fidelity: 0.8754 Trace: 0.8761\n",
      "Rep: 106 Cost: 0.1023 Fidelity: 0.8703 Trace: 0.8705\n",
      "Rep: 107 Cost: 0.1733 Fidelity: 0.8707 Trace: 0.8747\n",
      "Rep: 108 Cost: 0.1813 Fidelity: 0.8600 Trace: 0.8639\n",
      "Rep: 109 Cost: 0.0930 Fidelity: 0.8748 Trace: 0.8750\n",
      "Rep: 110 Cost: 0.1743 Fidelity: 0.8601 Trace: 0.8660\n",
      "Rep: 111 Cost: 0.1899 Fidelity: 0.8668 Trace: 0.8737\n",
      "Rep: 112 Cost: 0.1313 Fidelity: 0.8675 Trace: 0.8686\n",
      "Rep: 113 Cost: 0.1979 Fidelity: 0.8546 Trace: 0.8590\n",
      "Rep: 114 Cost: 0.1981 Fidelity: 0.8422 Trace: 0.8488\n",
      "Rep: 115 Cost: 0.1259 Fidelity: 0.8683 Trace: 0.8700\n",
      "Rep: 116 Cost: 0.1704 Fidelity: 0.8658 Trace: 0.8686\n",
      "Rep: 117 Cost: 0.2134 Fidelity: 0.8339 Trace: 0.8403\n",
      "Rep: 118 Cost: 0.1338 Fidelity: 0.8593 Trace: 0.8610\n",
      "Rep: 119 Cost: 0.1319 Fidelity: 0.8779 Trace: 0.8805\n",
      "Rep: 120 Cost: 0.1953 Fidelity: 0.8633 Trace: 0.8684\n",
      "Rep: 121 Cost: 0.1685 Fidelity: 0.8683 Trace: 0.8704\n",
      "Rep: 122 Cost: 0.1296 Fidelity: 0.8685 Trace: 0.8705\n",
      "Rep: 123 Cost: 0.1546 Fidelity: 0.8727 Trace: 0.8781\n",
      "Rep: 124 Cost: 0.1324 Fidelity: 0.8757 Trace: 0.8784\n",
      "Rep: 125 Cost: 0.0995 Fidelity: 0.8835 Trace: 0.8841\n",
      "Rep: 126 Cost: 0.1517 Fidelity: 0.8756 Trace: 0.8770\n",
      "Rep: 127 Cost: 0.1177 Fidelity: 0.8854 Trace: 0.8856\n",
      "Rep: 128 Cost: 0.1512 Fidelity: 0.8724 Trace: 0.8767\n",
      "Rep: 129 Cost: 0.1629 Fidelity: 0.8754 Trace: 0.8818\n",
      "Rep: 130 Cost: 0.1081 Fidelity: 0.8822 Trace: 0.8839\n",
      "Rep: 131 Cost: 0.1524 Fidelity: 0.8804 Trace: 0.8828\n",
      "Rep: 132 Cost: 0.2074 Fidelity: 0.8658 Trace: 0.8710\n",
      "Rep: 133 Cost: 0.1363 Fidelity: 0.8812 Trace: 0.8834\n",
      "Rep: 134 Cost: 0.0979 Fidelity: 0.8856 Trace: 0.8865\n",
      "Rep: 135 Cost: 0.1251 Fidelity: 0.8814 Trace: 0.8841\n",
      "Rep: 136 Cost: 0.0957 Fidelity: 0.8781 Trace: 0.8788\n",
      "Rep: 137 Cost: 0.1213 Fidelity: 0.8744 Trace: 0.8765\n",
      "Rep: 138 Cost: 0.1352 Fidelity: 0.8802 Trace: 0.8840\n",
      "Rep: 139 Cost: 0.0882 Fidelity: 0.8899 Trace: 0.8905\n",
      "Rep: 140 Cost: 0.1350 Fidelity: 0.8837 Trace: 0.8872\n",
      "Rep: 141 Cost: 0.1739 Fidelity: 0.8754 Trace: 0.8817\n",
      "Rep: 142 Cost: 0.1661 Fidelity: 0.8882 Trace: 0.8902\n",
      "Rep: 143 Cost: 0.1129 Fidelity: 0.8799 Trace: 0.8810\n",
      "Rep: 144 Cost: 0.1134 Fidelity: 0.8885 Trace: 0.8906\n",
      "Rep: 145 Cost: 0.1353 Fidelity: 0.8951 Trace: 0.8961\n",
      "Rep: 146 Cost: 0.1655 Fidelity: 0.8805 Trace: 0.8819\n",
      "Rep: 147 Cost: 0.1192 Fidelity: 0.8913 Trace: 0.8931\n",
      "Rep: 148 Cost: 0.0812 Fidelity: 0.8907 Trace: 0.8910\n",
      "Rep: 149 Cost: 0.1193 Fidelity: 0.8926 Trace: 0.8948\n",
      "Rep: 150 Cost: 0.1026 Fidelity: 0.8947 Trace: 0.8960\n",
      "Rep: 151 Cost: 0.1513 Fidelity: 0.8938 Trace: 0.8953\n",
      "Rep: 152 Cost: 0.1632 Fidelity: 0.8825 Trace: 0.8840\n",
      "Rep: 153 Cost: 0.0862 Fidelity: 0.8964 Trace: 0.8970\n",
      "Rep: 154 Cost: 0.1008 Fidelity: 0.9004 Trace: 0.9010\n",
      "Rep: 155 Cost: 0.1550 Fidelity: 0.8889 Trace: 0.8892\n",
      "Rep: 156 Cost: 0.0761 Fidelity: 0.8942 Trace: 0.8945\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rep: 157 Cost: 0.1401 Fidelity: 0.8995 Trace: 0.9006\n",
      "Rep: 158 Cost: 0.1367 Fidelity: 0.8952 Trace: 0.8962\n",
      "Rep: 159 Cost: 0.0876 Fidelity: 0.8967 Trace: 0.8972\n",
      "Rep: 160 Cost: 0.0851 Fidelity: 0.8968 Trace: 0.8976\n",
      "Rep: 161 Cost: 0.0691 Fidelity: 0.9022 Trace: 0.9025\n",
      "Rep: 162 Cost: 0.1176 Fidelity: 0.8998 Trace: 0.9024\n",
      "Rep: 163 Cost: 0.1544 Fidelity: 0.8917 Trace: 0.8940\n",
      "Rep: 164 Cost: 0.1110 Fidelity: 0.9007 Trace: 0.9010\n",
      "Rep: 165 Cost: 0.1048 Fidelity: 0.9007 Trace: 0.9023\n",
      "Rep: 166 Cost: 0.0944 Fidelity: 0.9015 Trace: 0.9029\n",
      "Rep: 167 Cost: 0.0903 Fidelity: 0.9000 Trace: 0.9004\n",
      "Rep: 168 Cost: 0.1470 Fidelity: 0.9057 Trace: 0.9075\n",
      "Rep: 169 Cost: 0.1353 Fidelity: 0.8974 Trace: 0.9005\n",
      "Rep: 170 Cost: 0.0837 Fidelity: 0.9078 Trace: 0.9078\n",
      "Rep: 171 Cost: 0.1598 Fidelity: 0.8917 Trace: 0.8966\n",
      "Rep: 172 Cost: 0.1713 Fidelity: 0.8908 Trace: 0.8991\n",
      "Rep: 173 Cost: 0.1366 Fidelity: 0.8960 Trace: 0.8996\n",
      "Rep: 174 Cost: 0.0976 Fidelity: 0.9067 Trace: 0.9069\n",
      "Rep: 175 Cost: 0.1244 Fidelity: 0.8966 Trace: 0.8978\n",
      "Rep: 176 Cost: 0.1247 Fidelity: 0.9020 Trace: 0.9032\n",
      "Rep: 177 Cost: 0.1105 Fidelity: 0.8943 Trace: 0.8947\n",
      "Rep: 178 Cost: 0.0993 Fidelity: 0.9094 Trace: 0.9097\n",
      "Rep: 179 Cost: 0.1255 Fidelity: 0.9001 Trace: 0.9012\n",
      "Rep: 180 Cost: 0.1044 Fidelity: 0.9055 Trace: 0.9059\n",
      "Rep: 181 Cost: 0.1232 Fidelity: 0.9012 Trace: 0.9029\n",
      "Rep: 182 Cost: 0.1322 Fidelity: 0.9062 Trace: 0.9079\n",
      "Rep: 183 Cost: 0.0909 Fidelity: 0.9039 Trace: 0.9040\n",
      "Rep: 184 Cost: 0.1013 Fidelity: 0.9061 Trace: 0.9065\n",
      "Rep: 185 Cost: 0.1069 Fidelity: 0.9027 Trace: 0.9034\n",
      "Rep: 186 Cost: 0.1115 Fidelity: 0.9098 Trace: 0.9105\n",
      "Rep: 187 Cost: 0.0885 Fidelity: 0.9037 Trace: 0.9039\n",
      "Rep: 188 Cost: 0.1042 Fidelity: 0.9096 Trace: 0.9103\n",
      "Rep: 189 Cost: 0.0970 Fidelity: 0.9042 Trace: 0.9043\n",
      "Rep: 190 Cost: 0.1091 Fidelity: 0.9110 Trace: 0.9116\n",
      "Rep: 191 Cost: 0.0828 Fidelity: 0.9058 Trace: 0.9059\n",
      "Rep: 192 Cost: 0.1076 Fidelity: 0.9108 Trace: 0.9114\n",
      "Rep: 193 Cost: 0.0987 Fidelity: 0.9048 Trace: 0.9049\n",
      "Rep: 194 Cost: 0.0893 Fidelity: 0.9095 Trace: 0.9097\n",
      "Rep: 195 Cost: 0.0969 Fidelity: 0.9052 Trace: 0.9058\n",
      "Rep: 196 Cost: 0.0911 Fidelity: 0.9115 Trace: 0.9115\n",
      "Rep: 197 Cost: 0.1106 Fidelity: 0.9045 Trace: 0.9053\n",
      "Rep: 198 Cost: 0.0771 Fidelity: 0.9111 Trace: 0.9112\n",
      "Rep: 199 Cost: 0.1156 Fidelity: 0.9021 Trace: 0.9037\n"
     ]
    }
   ],
   "source": [
    "fid_progress = []\n",
    "loss_progress = []\n",
    "best_fid = 0\n",
    "\n",
    "for i in range(reps):\n",
    "    # reset the engine if it has already been executed\n",
    "    if eng.run_progs:\n",
    "        eng.reset()\n",
    "\n",
    "    with tf.GradientTape() as tape:\n",
    "        loss, fid, ket, trace = cost(weights)\n",
    "\n",
    "    # Stores fidelity at each step\n",
    "    fid_progress.append(fid.numpy())\n",
    "    \n",
    "    loss_progress.append(loss)\n",
    "\n",
    "    if fid > best_fid:\n",
    "        # store the new best fidelity and best state\n",
    "        best_fid = fid.numpy()\n",
    "        learnt_state = ket.numpy()\n",
    "\n",
    "    # one repetition of the optimization\n",
    "    gradients = tape.gradient(loss, weights)\n",
    "    opt.apply_gradients(zip([gradients], [weights]))\n",
    "\n",
    "    # Prints progress at every rep\n",
    "    if i % 1 == 0:\n",
    "        print(\"Rep: {} Cost: {:.4f} Fidelity: {:.4f} Trace: {:.4f}\".format(i, loss, fid, trace))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16cc26b0",
   "metadata": {},
   "source": [
    "### Produce fidelity plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ab433d18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 6), dpi = 100)\n",
    "\n",
    "# Set tick font size\n",
    "for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    label.set_fontsize(16)\n",
    "\n",
    "plt.plot(fid_progress, linewidth = 3)\n",
    "plt.ylabel(\"Fidelity\", fontsize = '16')\n",
    "ax.yaxis.set_ticks(np.arange(0, 1, 0.4))\n",
    "plt.xlabel(\"Epoch\", fontsize = '16')\n",
    "ax.xaxis.set_ticks(np.arange(0, reps, 200))\n",
    "plt.title('STM Fidelity', fontsize = '20')\n",
    "\n",
    "\n",
    "plt.savefig('STM_fidelity.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18b4a851",
   "metadata": {},
   "source": [
    "### Produce loss plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3179404f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 12), dpi = 100)\n",
    "\n",
    "# Set tick font size\n",
    "for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    label.set_fontsize(16)\n",
    "\n",
    "plt.plot(loss_progress, linewidth = 3)\n",
    "plt.ylabel(\"Training Loss\", fontsize = '20')\n",
    "ax.yaxis.set_ticks(np.arange(0, 1, 0.4))\n",
    "plt.xlabel(\"Epoch\", fontsize = '20')\n",
    "ax.xaxis.set_ticks(np.arange(0, reps, 200))\n",
    "plt.title('STM Training Loss', fontsize = '24')\n",
    "\n",
    "\n",
    "plt.savefig('STM_loss.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89a1611d",
   "metadata": {},
   "source": [
    "### Definition of the function that plots Wigner functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "075b0ceb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "\n",
    "def wigner(rho):\n",
    "    \"\"\"This code is a modified version of the 'iterative' method\n",
    "    of the wigner function provided in QuTiP, which is released\n",
    "    under the BSD license, with the following copyright notice:\n",
    "\n",
    "    Copyright (C) 2011 and later, P.D. Nation, J.R. Johansson,\n",
    "    A.J.G. Pitchford, C. Granade, and A.L. Grimsmo.\n",
    "\n",
    "    All rights reserved.\"\"\"\n",
    "    import copy\n",
    "\n",
    "    # Domain parameter for Wigner function plots\n",
    "    l = 5.0\n",
    "    cutoff = rho.shape[0]\n",
    "\n",
    "    # Creates 2D grid for Wigner function plots\n",
    "    x = np.linspace(-l, l, 100)\n",
    "    p = np.linspace(-l, l, 100)\n",
    "\n",
    "    Q, P = np.meshgrid(x, p)\n",
    "    A = (Q + P * 1.0j) / (2 * np.sqrt(2 / 2))\n",
    "\n",
    "    Wlist = np.array([np.zeros(np.shape(A), dtype=complex) for k in range(cutoff)])\n",
    "\n",
    "    # Wigner function for |0><0|\n",
    "    Wlist[0] = np.exp(-2.0 * np.abs(A) ** 2) / np.pi\n",
    "\n",
    "    # W = rho(0,0)W(|0><0|)\n",
    "    W = np.real(rho[0, 0]) * np.real(Wlist[0])\n",
    "\n",
    "    for n in range(1, cutoff):\n",
    "        Wlist[n] = (2.0 * A * Wlist[n - 1]) / np.sqrt(n)\n",
    "        W += 2 * np.real(rho[0, n] * Wlist[n])\n",
    "\n",
    "    for m in range(1, cutoff):\n",
    "        temp = copy.copy(Wlist[m])\n",
    "        # Wlist[m] = Wigner function for |m><m|\n",
    "        Wlist[m] = (2 * np.conj(A) * temp - np.sqrt(m) * Wlist[m - 1]) / np.sqrt(m)\n",
    "\n",
    "        # W += rho(m,m)W(|m><m|)\n",
    "        W += np.real(rho[m, m] * Wlist[m])\n",
    "\n",
    "        for n in range(m + 1, cutoff):\n",
    "            temp2 = (2 * A * Wlist[n - 1] - np.sqrt(m) * temp) / np.sqrt(n)\n",
    "            temp = copy.copy(Wlist[n])\n",
    "            # Wlist[n] = Wigner function for |m><n|\n",
    "            Wlist[n] = temp2\n",
    "\n",
    "            # W += rho(m,n)W(|m><n|) + rho(n,m)W(|n><m|)\n",
    "            W += 2 * np.real(rho[m, n] * Wlist[n])\n",
    "\n",
    "    return Q, P, W / 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fb945aa",
   "metadata": {},
   "source": [
    "### Obtain the target and learnt states from the quantum decoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "caa236f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "rho_target = np.outer(target_state, target_state.conj())\n",
    "rho_learnt = np.outer(learnt_state, learnt_state.conj())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6161ade",
   "metadata": {},
   "source": [
    "### Plot the target state as a Wigner function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b9754c91",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bartu\\AppData\\Local\\Temp\\ipykernel_1808\\1595207717.py:7: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection=\"3d\")\n",
    "X, P, W = wigner(rho_target)\n",
    "ax.plot_surface(X, P, W, cmap=\"RdYlGn\", lw=0.5, rstride=1, cstride=1)\n",
    "ax.contour(X, P, W, 10, cmap=\"RdYlGn\", linestyles=\"solid\", offset=-0.17)\n",
    "ax.set_axis_off()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ea0eaaf",
   "metadata": {},
   "source": [
    "### Plot the learnt state as a Wigner function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "29c5a38a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bartu\\AppData\\Local\\Temp\\ipykernel_1808\\4117463628.py:7: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection=\"3d\")\n",
    "X, P, W = wigner(rho_learnt)\n",
    "ax.plot_surface(X, P, W, cmap=\"RdYlGn\", lw=0.5, rstride=1, cstride=1)\n",
    "ax.contour(X, P, W, 10, cmap=\"RdYlGn\", linestyles=\"solid\", offset=-0.17)\n",
    "ax.set_axis_off()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5f3bf787",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(save_folder_name + '/x' + str(train_state_select) + '.txt', X)\n",
    "np.savetxt(save_folder_name + '/p' + str(train_state_select) + '.txt', P)\n",
    "np.savetxt(save_folder_name + '/w' + str(train_state_select) + '.txt', W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acd815e9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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