docs/source/tutorials/mnist_qml_cn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "808fbde8",
   "metadata": {},
   "source": [
    "# MNIST 分类的量子机器学习"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b47d9ac",
   "metadata": {},
   "source": [
    "## 概述\n",
    "\n",
    "本教程的目的不是从机器学习的角度来更好地设计用于 MNIST 分类的量子机器学习方法。相反，我们使用一个简单的参数化电路，并演示 ``tensorcircuit``的量子机器学习相关的技术组件。此外，这个 jupyter notebook 绝不代表是量子机器学习的好的实践。\n",
    "[WIP note]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecb6d523",
   "metadata": {},
   "source": [
    "## 设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c22e2299",
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import jax\n",
    "from jax.config import config\n",
    "\n",
    "config.update(\"jax_enable_x64\", True)\n",
    "from jax import numpy as jnp\n",
    "import optax\n",
    "import tensorcircuit as tc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1708d3e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('complex128', 'float64')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tc.set_backend(\"tensorflow\")\n",
    "tc.set_dtype(\"complex128\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12d94f93",
   "metadata": {},
   "source": [
    "## 数据处理\n",
    "\n",
    "我们利用 MNIST 数据并将它们调整为 3*3 以适应 9 量子位电路。\n",
    "我们使用的测试平台是二进制分类任务，区分数字``1``与``5``。\n",
    "由于本教程不是关于量子机器学习的良好实践，因此我们将验证集放在一边。\n",
    "我们只为一个小型演示收集 100 个数据点用于实验。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d4fd8866",
   "metadata": {},
   "outputs": [],
   "source": [
    "# numpy 数据\n",
    "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
    "x_train = x_train[..., np.newaxis] / 255.0\n",
    "\n",
    "\n",
    "def filter_pair(x, y, a, b):\n",
    "    keep = (y == a) | (y == b)\n",
    "    x, y = x[keep], y[keep]\n",
    "    y = y == a\n",
    "    return x, y\n",
    "\n",
    "\n",
    "x_train, y_train = filter_pair(x_train, y_train, 1, 5)\n",
    "x_train_small = tf.image.resize(x_train, (3, 3)).numpy()\n",
    "x_train_bin = np.array(x_train_small > 0.5, dtype=np.float32)\n",
    "x_train_bin = np.squeeze(x_train_bin)[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "04bb8995",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n"
     ]
    }
   ],
   "source": [
    "# tensorflow 数据\n",
    "\n",
    "x_train_tf = tf.reshape(tf.constant(x_train_bin, dtype=tf.float64), [-1, 9])\n",
    "y_train_tf = tf.constant(y_train[:100], dtype=tf.float64)\n",
    "\n",
    "# jax 数据\n",
    "\n",
    "x_train_jax = jnp.array(x_train_bin, dtype=np.float64).reshape([100, -1])\n",
    "y_train_jax = jnp.array(y_train[:100], dtype=np.float64).reshape([100])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d361c04b",
   "metadata": {},
   "source": [
    "## 使用 ``vectorized_value_and_grad`` API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e77465de",
   "metadata": {},
   "outputs": [],
   "source": [
    "nlayers = 3\n",
    "\n",
    "\n",
    "def qml_loss(x, y, weights, nlayers):\n",
    "    n = 9\n",
    "    weights = tc.backend.cast(weights, \"complex128\")\n",
    "    x = tc.backend.cast(x, \"complex128\")\n",
    "    c = tc.Circuit(n)\n",
    "    for i in range(n):\n",
    "        c.rx(i, theta=x[i])\n",
    "    for j in range(nlayers):\n",
    "        for i in range(n - 1):\n",
    "            c.cnot(i, i + 1)\n",
    "        for i in range(n):\n",
    "            c.rx(i, theta=weights[2 * j, i])\n",
    "            c.ry(i, theta=weights[2 * j + 1, i])\n",
    "    ypred = c.expectation([tc.gates.z(), (4,)])\n",
    "    ypred = tc.backend.real(ypred)\n",
    "    ypred = (tc.backend.real(ypred) + 1) / 2.0\n",
    "    return -y * tc.backend.log(ypred) - (1 - y) * tc.backend.log(1 - ypred), ypred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1abbbada",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((<tf.Tensor: shape=(100,), dtype=float64, numpy=\n",
       "  array([0.8433698 , 0.56257199, 0.54653163, 0.56257199, 0.82036163,\n",
       "         0.56257199, 0.56257199, 0.58030506, 0.82036163, 0.56257199,\n",
       "         0.82036163, 0.56257199, 0.82036163, 0.56257199, 0.54653163,\n",
       "         0.54653163, 0.56257199, 0.56257199, 0.58030506, 0.82036163,\n",
       "         0.54653163, 0.56257199, 0.56257199, 0.56257199, 0.56257199,\n",
       "         0.56257199, 0.56257199, 0.85182866, 0.56257199, 0.82036163,\n",
       "         0.82036163, 0.56257199, 0.8433698 , 0.56257199, 0.8433698 ,\n",
       "         0.56257199, 0.85182866, 0.56257199, 0.82036163, 0.54653163,\n",
       "         0.56257199, 0.56257199, 0.56257199, 0.56257199, 0.8433698 ,\n",
       "         0.58030506, 0.56257199, 0.82036163, 0.8433698 , 0.8433698 ,\n",
       "         0.54653163, 0.56257199, 0.82036163, 0.86501404, 0.56257199,\n",
       "         0.56257199, 0.8433698 , 0.56257199, 0.85182866, 0.82036163,\n",
       "         0.82036163, 0.56257199, 0.82036163, 0.56257199, 0.56257199,\n",
       "         0.56257199, 0.82036163, 0.8433698 , 0.8433698 , 0.82036163,\n",
       "         0.56257199, 0.56257199, 0.56257199, 0.56257199, 0.56257199,\n",
       "         0.54653163, 0.86501404, 0.54653163, 0.54653163, 0.82036163,\n",
       "         0.56257199, 0.54653163, 0.8433698 , 0.54653163, 0.8433698 ,\n",
       "         0.56257199, 0.56257199, 0.8433698 , 0.82036163, 0.8433698 ,\n",
       "         0.56257199, 0.56257199, 0.56257199, 0.56257199, 0.56257199,\n",
       "         0.82036163, 0.56257199, 0.58030506, 0.8433698 , 0.56257199])>,\n",
       "  <tf.Tensor: shape=(100,), dtype=float64, numpy=\n",
       "  array([0.56974181, 0.56974181, 0.57895436, 0.56974181, 0.55972759,\n",
       "         0.56974181, 0.56974181, 0.55972759, 0.55972759, 0.56974181,\n",
       "         0.55972759, 0.56974181, 0.55972759, 0.56974181, 0.57895436,\n",
       "         0.57895436, 0.56974181, 0.56974181, 0.55972759, 0.55972759,\n",
       "         0.57895436, 0.56974181, 0.56974181, 0.56974181, 0.56974181,\n",
       "         0.56974181, 0.56974181, 0.57336595, 0.56974181, 0.55972759,\n",
       "         0.55972759, 0.56974181, 0.56974181, 0.56974181, 0.56974181,\n",
       "         0.56974181, 0.57336595, 0.56974181, 0.55972759, 0.57895436,\n",
       "         0.56974181, 0.56974181, 0.56974181, 0.56974181, 0.56974181,\n",
       "         0.55972759, 0.56974181, 0.55972759, 0.56974181, 0.56974181,\n",
       "         0.57895436, 0.56974181, 0.55972759, 0.57895436, 0.56974181,\n",
       "         0.56974181, 0.56974181, 0.56974181, 0.57336595, 0.55972759,\n",
       "         0.55972759, 0.56974181, 0.55972759, 0.56974181, 0.56974181,\n",
       "         0.56974181, 0.55972759, 0.56974181, 0.56974181, 0.55972759,\n",
       "         0.56974181, 0.56974181, 0.56974181, 0.56974181, 0.56974181,\n",
       "         0.57895436, 0.57895436, 0.57895436, 0.57895436, 0.55972759,\n",
       "         0.56974181, 0.57895436, 0.56974181, 0.57895436, 0.56974181,\n",
       "         0.56974181, 0.56974181, 0.56974181, 0.55972759, 0.56974181,\n",
       "         0.56974181, 0.56974181, 0.56974181, 0.56974181, 0.56974181,\n",
       "         0.55972759, 0.56974181, 0.55972759, 0.56974181, 0.56974181])>),\n",
       " [<tf.Tensor: shape=(6, 9), dtype=float64, numpy=\n",
       "  array([[ 5.79464357e-02,  1.12182823e-01,  8.13605755e-02,\n",
       "           1.52611620e-01,  1.13641690e+00, -1.41695736e+00,\n",
       "           7.62883290e-01,  4.44089210e-16,  3.33066907e-16],\n",
       "         [ 3.57155554e-02,  1.61488509e-01,  7.62331819e-02,\n",
       "           1.50335863e-01, -1.10363460e-01, -3.23606686e-01,\n",
       "          -3.14523756e-01,  1.11022302e-16, -2.22044605e-16],\n",
       "         [-3.04959149e-02,  6.03271869e-02,  2.47760477e-02,\n",
       "          -7.61859417e-02,  1.72064441e+00,  1.66891120e+00,\n",
       "           3.33066907e-16,  1.11022302e-16,  6.66133815e-16],\n",
       "         [-8.26503466e-03, -6.90338030e-02, -1.07589110e-01,\n",
       "           1.88650816e-01, -2.68228700e+00, -2.41159987e+00,\n",
       "           0.00000000e+00,  1.33226763e-15,  6.66133815e-16],\n",
       "         [-2.22044605e-16,  3.33066907e-16,  5.55111512e-16,\n",
       "          -3.33066907e-16,  4.02608813e-01, -7.77156117e-16,\n",
       "           3.33066907e-16,  1.11022302e-16,  3.33066907e-16],\n",
       "         [-6.66133815e-16,  9.99200722e-16,  2.22044605e-16,\n",
       "           5.55111512e-16, -1.11152817e+00,  1.11022302e-16,\n",
       "           4.44089210e-16,  1.11022302e-16,  8.88178420e-16]])>])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_qml_vvag():\n",
    "    qml_vvag = tc.backend.vectorized_value_and_grad(\n",
    "        qml_loss, argnums=(2,), vectorized_argnums=(0, 1), has_aux=True\n",
    "    )\n",
    "    qml_vvag = tc.backend.jit(qml_vvag, static_argnums=(3,))\n",
    "    return qml_vvag\n",
    "\n",
    "\n",
    "qml_vvag = get_qml_vvag()\n",
    "qml_vvag(x_train_tf, y_train_tf, tf.ones([nlayers * 2, 9], dtype=tf.float64), nlayers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d4db4a40",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %timeit qml_vvag(x_train_tf, y_train_tf, tf.ones([nlayers*2, 9], dtype=tf.float64), nlayers)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9ba763f",
   "metadata": {},
   "source": [
    "### Jax 后端兼容性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bbf3766f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorcircuit.backends.jax_backend.JaxBackend at 0x7ffb04a71820>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tc.set_backend(\"jax\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c5bd9e99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((DeviceArray([0.8433698 , 0.56257199, 0.54653163, 0.56257199, 0.82036163,\n",
       "               0.56257199, 0.56257199, 0.58030506, 0.82036163, 0.56257199,\n",
       "               0.82036163, 0.56257199, 0.82036163, 0.56257199, 0.54653163,\n",
       "               0.54653163, 0.56257199, 0.56257199, 0.58030506, 0.82036163,\n",
       "               0.54653163, 0.56257199, 0.56257199, 0.56257199, 0.56257199,\n",
       "               0.56257199, 0.56257199, 0.85182866, 0.56257199, 0.82036163,\n",
       "               0.82036163, 0.56257199, 0.8433698 , 0.56257199, 0.8433698 ,\n",
       "               0.56257199, 0.85182866, 0.56257199, 0.82036163, 0.54653163,\n",
       "               0.56257199, 0.56257199, 0.56257199, 0.56257199, 0.8433698 ,\n",
       "               0.58030506, 0.56257199, 0.82036163, 0.8433698 , 0.8433698 ,\n",
       "               0.54653163, 0.56257199, 0.82036163, 0.86501404, 0.56257199,\n",
       "               0.56257199, 0.8433698 , 0.56257199, 0.85182866, 0.82036163,\n",
       "               0.82036163, 0.56257199, 0.82036163, 0.56257199, 0.56257199,\n",
       "               0.56257199, 0.82036163, 0.8433698 , 0.8433698 , 0.82036163,\n",
       "               0.56257199, 0.56257199, 0.56257199, 0.56257199, 0.56257199,\n",
       "               0.54653163, 0.86501404, 0.54653163, 0.54653163, 0.82036163,\n",
       "               0.56257199, 0.54653163, 0.8433698 , 0.54653163, 0.8433698 ,\n",
       "               0.56257199, 0.56257199, 0.8433698 , 0.82036163, 0.8433698 ,\n",
       "               0.56257199, 0.56257199, 0.56257199, 0.56257199, 0.56257199,\n",
       "               0.82036163, 0.56257199, 0.58030506, 0.8433698 , 0.56257199],            dtype=float64),\n",
       "  DeviceArray([0.56974181, 0.56974181, 0.57895436, 0.56974181, 0.55972759,\n",
       "               0.56974181, 0.56974181, 0.55972759, 0.55972759, 0.56974181,\n",
       "               0.55972759, 0.56974181, 0.55972759, 0.56974181, 0.57895436,\n",
       "               0.57895436, 0.56974181, 0.56974181, 0.55972759, 0.55972759,\n",
       "               0.57895436, 0.56974181, 0.56974181, 0.56974181, 0.56974181,\n",
       "               0.56974181, 0.56974181, 0.57336595, 0.56974181, 0.55972759,\n",
       "               0.55972759, 0.56974181, 0.56974181, 0.56974181, 0.56974181,\n",
       "               0.56974181, 0.57336595, 0.56974181, 0.55972759, 0.57895436,\n",
       "               0.56974181, 0.56974181, 0.56974181, 0.56974181, 0.56974181,\n",
       "               0.55972759, 0.56974181, 0.55972759, 0.56974181, 0.56974181,\n",
       "               0.57895436, 0.56974181, 0.55972759, 0.57895436, 0.56974181,\n",
       "               0.56974181, 0.56974181, 0.56974181, 0.57336595, 0.55972759,\n",
       "               0.55972759, 0.56974181, 0.55972759, 0.56974181, 0.56974181,\n",
       "               0.56974181, 0.55972759, 0.56974181, 0.56974181, 0.55972759,\n",
       "               0.56974181, 0.56974181, 0.56974181, 0.56974181, 0.56974181,\n",
       "               0.57895436, 0.57895436, 0.57895436, 0.57895436, 0.55972759,\n",
       "               0.56974181, 0.57895436, 0.56974181, 0.57895436, 0.56974181,\n",
       "               0.56974181, 0.56974181, 0.56974181, 0.55972759, 0.56974181,\n",
       "               0.56974181, 0.56974181, 0.56974181, 0.56974181, 0.56974181,\n",
       "               0.55972759, 0.56974181, 0.55972759, 0.56974181, 0.56974181],            dtype=float64)),\n",
       " (DeviceArray([[ 5.79464357e-02,  1.12182823e-01,  8.13605755e-02,\n",
       "                 1.52611620e-01,  1.13641690e+00, -1.41695736e+00,\n",
       "                 7.62883290e-01,  1.01307851e-15, -1.03389519e-15],\n",
       "               [ 3.57155554e-02,  1.61488509e-01,  7.62331819e-02,\n",
       "                 1.50335863e-01, -1.10363460e-01, -3.23606686e-01,\n",
       "                -3.14523756e-01, -4.09394740e-16, -1.65145675e-15],\n",
       "               [-3.04959149e-02,  6.03271869e-02,  2.47760477e-02,\n",
       "                -7.61859417e-02,  1.72064441e+00,  1.66891120e+00,\n",
       "                 6.24500451e-16,  9.71445147e-16, -8.39606162e-16],\n",
       "               [-8.26503466e-03, -6.90338030e-02, -1.07589110e-01,\n",
       "                 1.88650816e-01, -2.68228700e+00, -2.41159987e+00,\n",
       "                -6.66133815e-16,  5.27355937e-16, -7.56339436e-16],\n",
       "               [-1.08246745e-15,  1.65839564e-15, -2.77555756e-16,\n",
       "                 1.38777878e-17,  4.02608813e-01, -7.14706072e-16,\n",
       "                -4.16333634e-17, -9.02056208e-16, -9.57567359e-16],\n",
       "               [-1.08940634e-15,  3.26128013e-16, -5.34294831e-16,\n",
       "                 6.93889390e-18, -1.11152817e+00,  3.19189120e-16,\n",
       "                -1.68615122e-15,  1.24900090e-16,  1.79717352e-15]],            dtype=float64),))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qml_vvag = get_qml_vvag()\n",
    "qml_vvag(\n",
    "    x_train_jax, y_train_jax, jnp.ones([nlayers * 2, 9], dtype=np.float64), nlayers\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "42a54943",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %timeit qml_vvag(x_train_jax, y_train_jax, jnp.ones([nlayers * 2, 9], dtype=np.float64), nlayers)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "589c356e",
   "metadata": {},
   "source": [
    "### 使用 ``tf.data`` 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "41dbd58b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 转换回 tensorflow\n",
    "tc.set_backend(\"tensorflow\")\n",
    "qml_vvag = get_qml_vvag()\n",
    "qml_vvag = tc.backend.jit(qml_vvag, static_argnums=(3,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "78a41c92",
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_data = (\n",
    "    tf.data.Dataset.from_tensor_slices((x_train_tf, y_train_tf))\n",
    "    .repeat(200)\n",
    "    .shuffle(100)\n",
    "    .batch(32)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a56aeb3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(0.689301607482696, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6825438352666904, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6815497367036047, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6632433448327015, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6641348270253142, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6779914200102861, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6550256969249619, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6801325087248677, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6190616725052769, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6711760566099414, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6965496746836946, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6443036572691725, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6060956714527996, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6728839286340991, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6584085272471567, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6600981577311038, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6581071758186605, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6609348320181809, shape=(), dtype=float64)\n",
      "tf.Tensor(0.5919640703180435, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6362392080775805, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6844038809425064, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6924617230085226, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6594653043250199, shape=(), dtype=float64)\n",
      "tf.Tensor(0.7076707818117074, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6730725215608222, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6565711271336594, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6665226844123278, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6368469891760338, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6499572506552256, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6110576844713855, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6312147945757532, shape=(), dtype=float64)\n",
      "tf.Tensor(0.6013772883771527, shape=(), dtype=float64)\n"
     ]
    }
   ],
   "source": [
    "opt = tf.keras.optimizers.Adam(1e-2)\n",
    "w = tf.Variable(\n",
    "    initial_value=tf.random.normal(shape=(2 * nlayers, 9), stddev=0.5, dtype=tf.float64)\n",
    ")\n",
    "for i, (xs, ys) in zip(range(2000), mnist_data):\n",
    "    (losses, ypreds), grad = qml_vvag(xs, ys, w, nlayers)\n",
    "    if i % 20 == 0:\n",
    "        print(tf.reduce_mean(losses))\n",
    "        opt.apply_gradients([(grad[0], w)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4088507",
   "metadata": {},
   "source": [
    "## 使用 ``tf.keras`` API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "47d33c93",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorcircuit import keras\n",
    "\n",
    "\n",
    "def qml_y(x, weights, nlayers):\n",
    "    n = 9\n",
    "    weights = tc.backend.cast(weights, \"complex128\")\n",
    "    x = tc.backend.cast(x, \"complex128\")\n",
    "    c = tc.Circuit(n)\n",
    "    for i in range(n):\n",
    "        c.rx(i, theta=x[i])\n",
    "    for j in range(nlayers):\n",
    "        for i in range(n - 1):\n",
    "            c.cnot(i, i + 1)\n",
    "        for i in range(n):\n",
    "            c.rx(i, theta=weights[2 * j, i])\n",
    "            c.ry(i, theta=weights[2 * j + 1, i])\n",
    "    ypred = c.expectation([tc.gates.z(), (4,)])\n",
    "    ypred = tc.backend.real(ypred)\n",
    "    ypred = (tc.backend.real(ypred) + 1) / 2.0\n",
    "    return ypred\n",
    "\n",
    "\n",
    "ql = keras.QuantumLayer(partial(qml_y, nlayers=nlayers), [(2 * nlayers, 9)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "952067b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(), dtype=float64, numpy=0.7179324626922607>,\n",
       " [<tf.Tensor: shape=(6, 9), dtype=float64, numpy=\n",
       "  array([[-1.97741333e-02, -3.24903196e-03, -1.19449484e-02,\n",
       "           1.34411790e-02, -2.29378194e-03,  9.24968875e-04,\n",
       "           3.41827505e-04,  1.38777878e-17, -6.93889390e-18],\n",
       "         [-1.85390086e-02,  3.81940052e-03, -3.05341288e-02,\n",
       "          -1.79981829e-03, -5.77913396e-02, -3.71762005e-03,\n",
       "          -5.10097165e-03, -1.71303943e-17, -1.73472348e-18],\n",
       "         [ 5.04193508e-03, -1.77846516e-02,  2.26429668e-02,\n",
       "          -1.41076421e-02, -3.13874407e-02,  1.37515418e-03,\n",
       "           2.08166817e-17,  2.42861287e-17, -1.73472348e-18],\n",
       "         [ 2.67860892e-02,  1.92311176e-02, -2.44580361e-02,\n",
       "          -5.08346256e-02, -1.15289797e-02, -8.99461139e-03,\n",
       "           3.46944695e-18, -5.20417043e-18, -6.93889390e-18],\n",
       "         [ 9.54097912e-18, -3.46944695e-18,  1.04083409e-17,\n",
       "          -1.73472348e-18, -2.53960212e-03,  1.31188463e-17,\n",
       "           5.20417043e-18, -1.38777878e-17, -1.04083409e-17],\n",
       "         [-2.60208521e-18,  5.20417043e-18, -5.20417043e-18,\n",
       "           5.20417043e-18, -3.82010017e-03,  1.38777878e-17,\n",
       "           1.73472348e-18, -2.08166817e-17,  6.93889390e-18]])>])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 带有 vag 范例的 keras 接口\n",
    "\n",
    "\n",
    "@tf.function\n",
    "def my_vvag(xs, ys):\n",
    "    with tf.GradientTape() as tape:\n",
    "        ypred = ql(xs)\n",
    "        loss = tf.keras.losses.BinaryCrossentropy()(ys, ypred)\n",
    "    return loss, tape.gradient(loss, ql.variables)\n",
    "\n",
    "\n",
    "my_vvag(x_train_tf, y_train_tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a70e2bca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %timeit my_vvag(x_train_tf, y_train_tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7621ea9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "4/4 [==============================] - 21s 8ms/step - loss: 0.7221 - binary_accuracy: 0.6016\n",
      "Epoch 2/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.7123 - binary_accuracy: 0.6016\n",
      "Epoch 3/100\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.7039 - binary_accuracy: 0.6562\n",
      "Epoch 4/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.7009 - binary_accuracy: 0.6562\n",
      "Epoch 5/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6979 - binary_accuracy: 0.6562\n",
      "Epoch 6/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6957 - binary_accuracy: 0.6016\n",
      "Epoch 7/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6935 - binary_accuracy: 0.4922\n",
      "Epoch 8/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6918 - binary_accuracy: 0.6562\n",
      "Epoch 9/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6910 - binary_accuracy: 0.7109\n",
      "Epoch 10/100\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.6901 - binary_accuracy: 0.5469\n",
      "Epoch 11/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6893 - binary_accuracy: 0.6016\n",
      "Epoch 12/100\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.6883 - binary_accuracy: 0.6562\n",
      "Epoch 13/100\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.6876 - binary_accuracy: 0.6016\n",
      "Epoch 14/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6869 - binary_accuracy: 0.5469\n",
      "Epoch 15/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6865 - binary_accuracy: 0.7109\n",
      "Epoch 16/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6858 - binary_accuracy: 0.6562\n",
      "Epoch 17/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6853 - binary_accuracy: 0.5469\n",
      "Epoch 18/100\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.6847 - binary_accuracy: 0.6016\n",
      "Epoch 19/100\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.6844 - binary_accuracy: 0.6016\n",
      "Epoch 20/100\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.6842 - binary_accuracy: 0.5469\n",
      "Epoch 21/100\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.6841 - binary_accuracy: 0.6016\n",
      "Epoch 22/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6839 - binary_accuracy: 0.7109\n",
      "Epoch 23/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6835 - binary_accuracy: 0.6562\n",
      "Epoch 24/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6829 - binary_accuracy: 0.6016\n",
      "Epoch 25/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6823 - binary_accuracy: 0.7109\n",
      "Epoch 26/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6816 - binary_accuracy: 0.6016\n",
      "Epoch 27/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6811 - binary_accuracy: 0.5469\n",
      "Epoch 28/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6805 - binary_accuracy: 0.4922\n",
      "Epoch 29/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6803 - binary_accuracy: 0.6562\n",
      "Epoch 30/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6799 - binary_accuracy: 0.5469\n",
      "Epoch 31/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.6795 - binary_accuracy: 0.6016\n",
      "Epoch 32/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.6793 - binary_accuracy: 0.5469\n",
      "Epoch 33/100\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.6789 - binary_accuracy: 0.6016\n",
      "Epoch 34/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6785 - binary_accuracy: 0.5469\n",
      "Epoch 35/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6781 - binary_accuracy: 0.6562\n",
      "Epoch 36/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6775 - binary_accuracy: 0.5469\n",
      "Epoch 37/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6762 - binary_accuracy: 0.6016\n",
      "Epoch 38/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6752 - binary_accuracy: 0.6562\n",
      "Epoch 39/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6736 - binary_accuracy: 0.6016\n",
      "Epoch 40/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6714 - binary_accuracy: 0.6562\n",
      "Epoch 41/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6690 - binary_accuracy: 0.6562\n",
      "Epoch 42/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6658 - binary_accuracy: 0.6016\n",
      "Epoch 43/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6637 - binary_accuracy: 0.6016\n",
      "Epoch 44/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6617 - binary_accuracy: 0.6562\n",
      "Epoch 45/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6596 - binary_accuracy: 0.6016\n",
      "Epoch 46/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6586 - binary_accuracy: 0.6016\n",
      "Epoch 47/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6571 - binary_accuracy: 0.6016\n",
      "Epoch 48/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6561 - binary_accuracy: 0.6562\n",
      "Epoch 49/100\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.6549 - binary_accuracy: 0.6562\n",
      "Epoch 50/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6536 - binary_accuracy: 0.6562\n",
      "Epoch 51/100\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.6536 - binary_accuracy: 0.6562\n",
      "Epoch 52/100\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.6519 - binary_accuracy: 0.6016\n",
      "Epoch 53/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6516 - binary_accuracy: 0.7109\n",
      "Epoch 54/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6504 - binary_accuracy: 0.6016\n",
      "Epoch 55/100\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.6500 - binary_accuracy: 0.6016\n",
      "Epoch 56/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6486 - binary_accuracy: 0.5469\n",
      "Epoch 57/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6468 - binary_accuracy: 0.6016\n",
      "Epoch 58/100\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.6466 - binary_accuracy: 0.7109\n",
      "Epoch 59/100\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.6456 - binary_accuracy: 0.6562\n",
      "Epoch 60/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6446 - binary_accuracy: 0.7109\n",
      "Epoch 61/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6435 - binary_accuracy: 0.6016\n",
      "Epoch 62/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.6429 - binary_accuracy: 0.7109\n",
      "Epoch 63/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.6417 - binary_accuracy: 0.7109\n",
      "Epoch 64/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.6432 - binary_accuracy: 0.5469\n",
      "Epoch 65/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6439 - binary_accuracy: 0.7109\n",
      "Epoch 66/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6430 - binary_accuracy: 0.6016\n",
      "Epoch 67/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.6415 - binary_accuracy: 0.5469\n",
      "Epoch 68/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6391 - binary_accuracy: 0.6562\n",
      "Epoch 69/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.6375 - binary_accuracy: 0.6016\n",
      "Epoch 70/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6372 - binary_accuracy: 0.6016\n",
      "Epoch 71/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6369 - binary_accuracy: 0.5469\n",
      "Epoch 72/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6367 - binary_accuracy: 0.6016\n",
      "Epoch 73/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6380 - binary_accuracy: 0.7109\n",
      "Epoch 74/100\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.6377 - binary_accuracy: 0.6562\n",
      "Epoch 75/100\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.6365 - binary_accuracy: 0.6562\n",
      "Epoch 76/100\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.6350 - binary_accuracy: 0.6562\n",
      "Epoch 77/100\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.6331 - binary_accuracy: 0.6016\n",
      "Epoch 78/100\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.6331 - binary_accuracy: 0.6562\n",
      "Epoch 79/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6337 - binary_accuracy: 0.5469\n",
      "Epoch 80/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6319 - binary_accuracy: 0.6562\n",
      "Epoch 81/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6317 - binary_accuracy: 0.7109\n",
      "Epoch 82/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.6312 - binary_accuracy: 0.6562\n",
      "Epoch 83/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.6307 - binary_accuracy: 0.6562\n",
      "Epoch 84/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6326 - binary_accuracy: 0.6016\n",
      "Epoch 85/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6307 - binary_accuracy: 0.6016\n",
      "Epoch 86/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.6299 - binary_accuracy: 0.6016\n",
      "Epoch 87/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6288 - binary_accuracy: 0.6016\n",
      "Epoch 88/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6288 - binary_accuracy: 0.7109\n",
      "Epoch 89/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.6289 - binary_accuracy: 0.6562\n",
      "Epoch 90/100\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.6273 - binary_accuracy: 0.6562\n",
      "Epoch 91/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6275 - binary_accuracy: 0.5469\n",
      "Epoch 92/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6269 - binary_accuracy: 0.7109\n",
      "Epoch 93/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6269 - binary_accuracy: 0.6016\n",
      "Epoch 94/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6263 - binary_accuracy: 0.6016\n",
      "Epoch 95/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.6258 - binary_accuracy: 0.6016\n",
      "Epoch 96/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6256 - binary_accuracy: 0.6562\n",
      "Epoch 97/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6250 - binary_accuracy: 0.6562\n",
      "Epoch 98/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.6246 - binary_accuracy: 0.7109\n",
      "Epoch 99/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.6240 - binary_accuracy: 0.6562\n",
      "Epoch 100/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.6251 - binary_accuracy: 0.5469\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7ffaf92858b0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# keras 接口与 keras 训练范例\n",
    "\n",
    "model = tf.keras.Sequential([ql])\n",
    "\n",
    "model.compile(\n",
    "    loss=tf.keras.losses.BinaryCrossentropy(),\n",
    "    optimizer=tf.keras.optimizers.Adam(0.01),\n",
    "    metrics=[tf.keras.metrics.BinaryAccuracy()],\n",
    ")\n",
    "\n",
    "model.fit(x_train_tf, y_train_tf, batch_size=32, epochs=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "462295ea",
   "metadata": {},
   "source": [
    "### Keras 中的量子经典混合模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "afdc3791",
   "metadata": {},
   "outputs": [],
   "source": [
    "def qml_ys(x, weights, nlayers):\n",
    "    n = 9\n",
    "    weights = tc.backend.cast(weights, \"complex128\")\n",
    "    x = tc.backend.cast(x, \"complex128\")\n",
    "    c = tc.Circuit(n)\n",
    "    for i in range(n):\n",
    "        c.rx(i, theta=x[i])\n",
    "    for j in range(nlayers):\n",
    "        for i in range(n - 1):\n",
    "            c.cnot(i, i + 1)\n",
    "        for i in range(n):\n",
    "            c.rx(i, theta=weights[2 * j, i])\n",
    "            c.ry(i, theta=weights[2 * j + 1, i])\n",
    "    ypreds = []\n",
    "    for i in range(n):\n",
    "        ypred = c.expectation([tc.gates.z(), (i,)])\n",
    "        ypred = tc.backend.real(ypred)\n",
    "        ypred = (tc.backend.real(ypred) + 1) / 2.0\n",
    "        ypreds.append(ypred)\n",
    "    return tc.backend.stack(ypreds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8ab1937b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ql = tc.keras.QuantumLayer(partial(qml_ys, nlayers=nlayers), [(2 * nlayers, 9)])\n",
    "model = tf.keras.Sequential([ql, tf.keras.layers.Dense(1, activation=\"sigmoid\")])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "803d96ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "4/4 [==============================] - 24s 14ms/step - loss: 0.9307 - binary_accuracy: 0.3700\n",
      "Epoch 2/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.8286 - binary_accuracy: 0.3700\n",
      "Epoch 3/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.7538 - binary_accuracy: 0.3700\n",
      "Epoch 4/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.7044 - binary_accuracy: 0.3700\n",
      "Epoch 5/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6796 - binary_accuracy: 0.6300\n",
      "Epoch 6/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6599 - binary_accuracy: 0.6300\n",
      "Epoch 7/100\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.6543 - binary_accuracy: 0.6300\n",
      "Epoch 8/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6559 - binary_accuracy: 0.6300\n",
      "Epoch 9/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6575 - binary_accuracy: 0.6300\n",
      "Epoch 10/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6588 - binary_accuracy: 0.6300\n",
      "Epoch 11/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6587 - binary_accuracy: 0.6300\n",
      "Epoch 12/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6567 - binary_accuracy: 0.6300\n",
      "Epoch 13/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6551 - binary_accuracy: 0.6300\n",
      "Epoch 14/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6540 - binary_accuracy: 0.6300\n",
      "Epoch 15/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6528 - binary_accuracy: 0.6300\n",
      "Epoch 16/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6533 - binary_accuracy: 0.6300\n",
      "Epoch 17/100\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.6540 - binary_accuracy: 0.6300\n",
      "Epoch 18/100\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.6550 - binary_accuracy: 0.6300\n",
      "Epoch 19/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6546 - binary_accuracy: 0.6300\n",
      "Epoch 20/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6538 - binary_accuracy: 0.6300\n",
      "Epoch 21/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6513 - binary_accuracy: 0.6300\n",
      "Epoch 22/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6499 - binary_accuracy: 0.6300\n",
      "Epoch 23/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6497 - binary_accuracy: 0.6300\n",
      "Epoch 24/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6491 - binary_accuracy: 0.6300\n",
      "Epoch 25/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6492 - binary_accuracy: 0.6300\n",
      "Epoch 26/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6493 - binary_accuracy: 0.6300\n",
      "Epoch 27/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6487 - binary_accuracy: 0.6300\n",
      "Epoch 28/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6473 - binary_accuracy: 0.6300\n",
      "Epoch 29/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6470 - binary_accuracy: 0.6300\n",
      "Epoch 30/100\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 0.6462 - binary_accuracy: 0.6300\n",
      "Epoch 31/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6451 - binary_accuracy: 0.6300\n",
      "Epoch 32/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6439 - binary_accuracy: 0.6300\n",
      "Epoch 33/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6433 - binary_accuracy: 0.6300\n",
      "Epoch 34/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6438 - binary_accuracy: 0.6300\n",
      "Epoch 35/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6413 - binary_accuracy: 0.6300\n",
      "Epoch 36/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6403 - binary_accuracy: 0.6300\n",
      "Epoch 37/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6391 - binary_accuracy: 0.6300\n",
      "Epoch 38/100\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 0.6388 - binary_accuracy: 0.6300\n",
      "Epoch 39/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6379 - binary_accuracy: 0.6300\n",
      "Epoch 40/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6365 - binary_accuracy: 0.6300\n",
      "Epoch 41/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6352 - binary_accuracy: 0.6300\n",
      "Epoch 42/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6336 - binary_accuracy: 0.6300\n",
      "Epoch 43/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6338 - binary_accuracy: 0.6300\n",
      "Epoch 44/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6358 - binary_accuracy: 0.6300\n",
      "Epoch 45/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6367 - binary_accuracy: 0.6300\n",
      "Epoch 46/100\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.6345 - binary_accuracy: 0.6300\n",
      "Epoch 47/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6303 - binary_accuracy: 0.6300\n",
      "Epoch 48/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6298 - binary_accuracy: 0.6300\n",
      "Epoch 49/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6285 - binary_accuracy: 0.6300\n",
      "Epoch 50/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6280 - binary_accuracy: 0.6300\n",
      "Epoch 51/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6274 - binary_accuracy: 0.6300\n",
      "Epoch 52/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6268 - binary_accuracy: 0.6300\n",
      "Epoch 53/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6262 - binary_accuracy: 0.6300\n",
      "Epoch 54/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6246 - binary_accuracy: 0.6300\n",
      "Epoch 55/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6231 - binary_accuracy: 0.6300\n",
      "Epoch 56/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6228 - binary_accuracy: 0.6300\n",
      "Epoch 57/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6226 - binary_accuracy: 0.6300\n",
      "Epoch 58/100\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.6224 - binary_accuracy: 0.6300\n",
      "Epoch 59/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6228 - binary_accuracy: 0.6900\n",
      "Epoch 60/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6224 - binary_accuracy: 0.7200\n",
      "Epoch 61/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6214 - binary_accuracy: 0.7200\n",
      "Epoch 62/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6183 - binary_accuracy: 0.6300\n",
      "Epoch 63/100\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.6161 - binary_accuracy: 0.6300\n",
      "Epoch 64/100\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.6142 - binary_accuracy: 0.6300\n",
      "Epoch 65/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6131 - binary_accuracy: 0.6300\n",
      "Epoch 66/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6124 - binary_accuracy: 0.6300\n",
      "Epoch 67/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6101 - binary_accuracy: 0.6300\n",
      "Epoch 68/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6117 - binary_accuracy: 0.6600\n",
      "Epoch 69/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6099 - binary_accuracy: 0.7200\n",
      "Epoch 70/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6085 - binary_accuracy: 0.7200\n",
      "Epoch 71/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6070 - binary_accuracy: 0.7200\n",
      "Epoch 72/100\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.6069 - binary_accuracy: 0.7200\n",
      "Epoch 73/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6060 - binary_accuracy: 0.7200\n",
      "Epoch 74/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6040 - binary_accuracy: 0.7200\n",
      "Epoch 75/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6041 - binary_accuracy: 0.7200\n",
      "Epoch 76/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.6011 - binary_accuracy: 0.6900\n",
      "Epoch 77/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.6005 - binary_accuracy: 0.6300\n",
      "Epoch 78/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.5993 - binary_accuracy: 0.6300\n",
      "Epoch 79/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.5987 - binary_accuracy: 0.6300\n",
      "Epoch 80/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.5982 - binary_accuracy: 0.6300\n",
      "Epoch 81/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.5970 - binary_accuracy: 0.6300\n",
      "Epoch 82/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.5960 - binary_accuracy: 0.6900\n",
      "Epoch 83/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.5937 - binary_accuracy: 0.7200\n",
      "Epoch 84/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.5932 - binary_accuracy: 0.7200\n",
      "Epoch 85/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.5913 - binary_accuracy: 0.7200\n",
      "Epoch 86/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.5904 - binary_accuracy: 0.7200\n",
      "Epoch 87/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.5895 - binary_accuracy: 0.7200\n",
      "Epoch 88/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.5882 - binary_accuracy: 0.7200\n",
      "Epoch 89/100\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.5873 - binary_accuracy: 0.7200\n",
      "Epoch 90/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.5858 - binary_accuracy: 0.7200\n",
      "Epoch 91/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.5848 - binary_accuracy: 0.7200\n",
      "Epoch 92/100\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 0.5839 - binary_accuracy: 0.7200\n",
      "Epoch 93/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.5832 - binary_accuracy: 0.7200\n",
      "Epoch 94/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.5836 - binary_accuracy: 0.7200\n",
      "Epoch 95/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.5848 - binary_accuracy: 0.7200\n",
      "Epoch 96/100\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.5836 - binary_accuracy: 0.7200\n",
      "Epoch 97/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.5812 - binary_accuracy: 0.7200\n",
      "Epoch 98/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.5795 - binary_accuracy: 0.7200\n",
      "Epoch 99/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.5780 - binary_accuracy: 0.7200\n",
      "Epoch 100/100\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.5768 - binary_accuracy: 0.7200\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7ffac3142e20>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(\n",
    "    loss=tf.keras.losses.BinaryCrossentropy(),\n",
    "    optimizer=tf.keras.optimizers.Adam(0.01),\n",
    "    metrics=[tf.keras.metrics.BinaryAccuracy()],\n",
    ")\n",
    "\n",
    "model.fit(x_train_tf, y_train_tf, batch_size=32, epochs=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "742aaa4c",
   "metadata": {},
   "source": [
    "### Jax 中的混合模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e52bc886",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorcircuit.backends.jax_backend.JaxBackend at 0x7ffb04a71820>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tc.set_backend(\"jax\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "54257130",
   "metadata": {},
   "outputs": [],
   "source": [
    "key = jax.random.PRNGKey(42)\n",
    "key, *subkeys = jax.random.split(key, num=4)\n",
    "params = {\n",
    "    \"qweights\": jax.random.normal(subkeys[0], shape=[nlayers * 2, 9]),\n",
    "    \"cweights:w\": jax.random.normal(subkeys[1], shape=[9]),\n",
    "    \"cweights:b\": jax.random.normal(subkeys[2], shape=[1]),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "88cd6d3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def qml_hybrid_loss(x, y, params, nlayers):\n",
    "    weights = params[\"qweights\"]\n",
    "    w = params[\"cweights:w\"]\n",
    "    b = params[\"cweights:b\"]\n",
    "    ypred = qml_ys(x, weights, nlayers)\n",
    "    ypred = tc.backend.reshape(ypred, [-1, 1])\n",
    "    ypred = w @ ypred + b\n",
    "    ypred = jax.nn.sigmoid(ypred)\n",
    "    ypred = ypred[0]\n",
    "    loss = -y * tc.backend.log(ypred) - (1 - y) * tc.backend.log(1 - ypred)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "240b12cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "qml_hybrid_loss_vag = tc.backend.jit(\n",
    "    tc.backend.vvag(qml_hybrid_loss, vectorized_argnums=(0, 1), argnums=2),\n",
    "    static_argnums=3,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a0736ccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(DeviceArray([3.73282398, 0.02421603, 0.02899787, 0.02421603, 4.08996787,\n",
       "              0.03069481, 0.02421603, 0.01688146, 4.08996787, 0.03069481,\n",
       "              4.08996787, 0.02421603, 4.08996787, 0.02421603, 0.02899787,\n",
       "              0.03354042, 0.02421603, 0.02421603, 0.01688146, 4.08996787,\n",
       "              0.03354042, 0.02421603, 0.02421603, 0.03069481, 0.02421603,\n",
       "              0.02421603, 0.03069481, 3.73798651, 0.02421603, 3.68810189,\n",
       "              4.08996787, 0.03069481, 3.73282398, 0.03069481, 3.73282398,\n",
       "              0.02421603, 3.49674264, 0.02421603, 4.08996787, 0.02899787,\n",
       "              0.02421603, 0.02421603, 0.03069481, 0.03069481, 3.73282398,\n",
       "              0.02533775, 0.03069481, 3.68810189, 3.73282398, 3.49896983,\n",
       "              0.02899787, 0.03069481, 4.08996787, 3.41172721, 0.02421603,\n",
       "              0.02421603, 3.73282398, 0.02421603, 3.73798651, 3.68810189,\n",
       "              4.08996787, 0.03069481, 4.08996787, 0.02421603, 0.03069481,\n",
       "              0.02421603, 3.68810189, 3.49896983, 3.49896983, 4.08996787,\n",
       "              0.02421603, 0.02421603, 0.02421603, 0.02421603, 0.03069481,\n",
       "              0.02899787, 3.41172721, 0.03354042, 0.02899787, 3.68810189,\n",
       "              0.02421603, 0.03354042, 3.73282398, 0.02899787, 3.73282398,\n",
       "              0.03069481, 0.02421603, 3.73282398, 3.68810189, 3.73282398,\n",
       "              0.02421603, 0.02421603, 0.03069481, 0.03069481, 0.02421603,\n",
       "              4.08996787, 0.02421603, 0.01688146, 3.73282398, 0.02421603],            dtype=float64),\n",
       " {'cweights:b': DeviceArray([34.49476789], dtype=float64),\n",
       "  'cweights:w': DeviceArray([16.81782277, 15.05718878, 15.02498328, 23.18351696,\n",
       "               17.01897109, 16.13466029, 16.26046722, 23.54180309,\n",
       "               12.0721068 ], dtype=float64),\n",
       "  'qweights': DeviceArray([[-1.16993912e+01, -6.74730815e+00, -2.27227872e+00,\n",
       "                -1.08703899e+00,  2.56625721e+00,  1.69462223e+00,\n",
       "                -4.89847061e+00,  1.62487935e+00,  1.02424785e+01],\n",
       "               [ 3.29984130e+00, -5.90635608e-01,  2.11407610e+00,\n",
       "                 3.67096431e-02,  3.32526833e+00, -1.06468920e+00,\n",
       "                -4.12299772e-01, -7.78105081e+00, -3.38506241e+00],\n",
       "               [-3.59434442e+00,  3.84548015e+00,  8.50409406e-01,\n",
       "                -2.66504333e+00,  1.47559967e+00,  1.38536529e+00,\n",
       "                -1.47291602e-01, -7.32213541e+00,  5.17021200e+00],\n",
       "               [-1.30975045e+00,  1.83003338e+00,  1.51443252e+00,\n",
       "                 3.15082430e+00, -4.41767236e+00,  6.25968228e+00,\n",
       "                 5.96980281e+00,  9.67198061e+00, -1.63091455e+01],\n",
       "               [-2.24757712e+00, -5.66276080e-01, -1.67376432e+00,\n",
       "                 1.75249049e-01,  2.77917505e-01,  3.84402979e-02,\n",
       "                 1.03434679e-01, -4.05760762e-02, -3.33671956e-03],\n",
       "               [ 3.13599600e+00,  3.85470136e+00,  3.17986238e-01,\n",
       "                 1.72308312e-01,  5.09749793e+00,  2.90706770e-02,\n",
       "                -5.59919189e-01,  1.96734688e+00, -6.96372626e-01]],            dtype=float64)})"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qml_hybrid_loss_vag(x_train_jax, y_train_jax, params, nlayers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a2944d70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.2979572281332594\n",
      "0.8331012068009501\n",
      "0.6805939758448183\n",
      "0.5897353928152392\n",
      "0.6460840124038746\n",
      "0.6093143713632384\n",
      "0.6671721223530598\n",
      "0.5863347320393952\n",
      "0.5465362554431986\n",
      "0.5594138744621404\n",
      "0.5493311423294576\n",
      "0.5228166702417829\n",
      "0.6176455570797168\n",
      "0.5256494465741394\n",
      "0.5359881696740493\n",
      "0.5787532611935906\n",
      "0.49082340457493323\n",
      "0.4062487079116086\n",
      "0.5802733401377229\n",
      "0.4762524476616207\n",
      "0.5404245247888219\n"
     ]
    }
   ],
   "source": [
    "optimizer = optax.adam(5e-3)\n",
    "opt_state = optimizer.init(params)\n",
    "for i, (xs, ys) in zip(range(2000), mnist_data):  # 在这里使用 tensorflow 数据加载器\n",
    "    xs = xs.numpy()\n",
    "    ys = ys.numpy()\n",
    "    v, grads = qml_hybrid_loss_vag(xs, ys, params, nlayers)\n",
    "    updates, opt_state = optimizer.update(grads, opt_state)\n",
    "    params = optax.apply_updates(params, updates)\n",
    "    if i % 30 == 0:\n",
    "        print(jnp.mean(v))"
   ]
  }
 ],
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