{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 量子神经网络初体验\n", "\n", "[![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r1.7/resource/_static/logo_source.png)](https://gitee.com/mindspore/docs/blob/r1.7/docs/mindquantum/docs/source_zh_cn/initial_experience_of_quantum_neural_network.ipynb)\n", "\n", "## 量子神经网络的结构\n", "\n", "在MindQuantum中，量子神经网络（Quantum Neural Network, QNN）的结构如下图所示，其通常由三部分构成：\n", "\n", "（1）一个（或多个）编码线路，用于将经典数据编码到量子数据（通常称为Encoder）；\n", "\n", "（2）一个（或多个）训练线路，用于训练带参量子门中的参数（通常称为Ansatz）；\n", "\n", "（3）一个（或多个）测量，用于检测测量值（例如在`Z`方向上测量，就是某个量子比特的量子态在`Z`轴上的投影，该测量得到的是量子态关于泡利`Z`算符（不限定于泡利`Z`算符，换成其它的算符亦可）的期望值）是否接近于目标期望值。\n", "\n", "![mindquantum](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r1.7/docs/mindquantum/docs/source_zh_cn/images/mindquantum.png)\n", "\n", "下面，我们通过一个简单的例子来体验一下如何使用MindQuantum。\n", "\n", "## 简单的例子\n", "\n", "![example circuit](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r1.7/docs/mindquantum/docs/source_zh_cn/images/example_circuit.png)\n", "\n", "我们搭建如上图所示的量子神经网络，其中Encoder由一个`H`门，1个`RX`门、1个`RY`门和1个`RZ`门构成，Ansatz由1个`RX`门和1个`RY`门构成，测量则是作用在第0位量子比特上的泡利`Z`算符。\n", "\n", "问题描述：我们将Encoder看成是系统对初始量子态的误差影响（参数$\\alpha_0, \\alpha_1$​和$\\alpha_2$​是将原经典数据经过预处理（可选）后得到的某个固定值，即为已知值，在此分别设为0.2, 0.3和0.4）。我们需要训练一个Ansatz来抵消掉这个误差，使得最后的量子态还是处于$|0\\rangle$​态。\n", "\n", "思路：对末态执行泡利`Z`算符测量，此时的测量值就是此时的量子态关于泡利`Z`算符的期望值。由于$|0\\rangle$​​是算符`Z`的本征态，且本征值为1，容易知道\n", "\n", "$$\n", "\\langle 0|Z|0\\rangle=1.\n", "$$\n", "\n", "也就是说，目标期望值为1。可以通过测量得到的期望值来验证此时的状态是否为$|0\\rangle$。\n", "\n", "解决方案：通过训练Ansatz中的参数，希望测量值接近于目标期望值，换句话说，我们只需让测量值尽可能接近于$|0\\rangle$态关于泡利`Z`算符对应的期望值，那么此时的状态就是$|0\\rangle$，即Ansatz抵消了Encoder对初始量子态产生的误差。\n", "\n", "## 环境准备\n", "\n", "导入本教程所依赖的模块" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np # 导入numpy库并简写为np\n", "from mindquantum.core import Circuit # 导入Circuit模块，用于搭建量子线路\n", "from mindquantum.core import H, RX, RY, RZ # 导入量子门H, RX, RY, RZ" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 搭建Encoder\n", "\n", "根据图示的量子线路图，我们可以在MindQuantum中搭建Encoder。" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "==================Circuit Summary==================\n", "|Total number of gates : 4. |\n", "|Parameter gates : 3. |\n", "|with 3 parameters are : alpha0, alpha1, alpha2. |\n", "|Number qubit of circuit: 1 |\n", "===================================================\n" ] }, { "data": { "text/html": [ "
q0: ──H────RX(alpha0)────RY(alpha1)────RZ(alpha2)──\n",
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\n" ], "text/plain": [ "q0: ──H────RX(alpha0)────RY(alpha1)────RZ(alpha2)──" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# pylint: disable=W0104\n", "encoder = Circuit() # 初始化量子线路\n", "encoder += H.on(0) # H门作用在第0位量子比特\n", "encoder += RX(f'alpha{0}').on(0) # RX(alpha_0)门作用在第0位量子比特\n", "encoder += RY(f'alpha{1}').on(0) # RY(alpha_1)门作用在第0位量子比特\n", "encoder += RZ(f'alpha{2}').on(0) # RZ(alpha_2)门作用在第0位量子比特\n", "encoder = encoder.no_grad() # Encoder作为整个量子神经网络的第一层，不用对编码线路中的梯度求导数，因此加入no_grad()\n", "encoder.summary() # 总结Encoder\n", "encoder" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "从对Encoder的Summary中可以看到，该量子线路由4个量子门组成，其中有3个含参量子门且参数为$\\alpha_0,\\alpha_1,\\alpha_2$​​​​，该量子线路调控的量子比特数为1。\n", "\n", "然后，我们需要对Encoder中的参数进行赋值。由于Encoder中的参数$\\alpha_0, \\alpha_1$​和$\\alpha_2$​分别为已知值0.2, 0.3和0.4，因此可以直接对参数进行赋值，并打印此时的状态。" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0.5669903122552596-0.1753906567580312j)¦0⟩\n", "(0.800814626197614+0.08034947292077024j)¦1⟩\n" ] } ], "source": [ "alpha0, alpha1, alpha2 = 0.2, 0.3, 0.4 # alpha0, alpha1, alpha2为已知的固定值，分别赋值0.2, 0.3 和0.4\n", "state = encoder.get_qs(pr={'alpha0': alpha0, 'alpha1': alpha1, 'alpha2': alpha2}, ket=True)\n", "print(state)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "上述步骤为了展示MindQuantum可以演化量子线路（若量子线路中的量子门带参数，则需要对参数赋值）并得到演化后的末态。从上述打印可以看到，演化后得到的末态为$|0\\rangle$​​​和$|1\\rangle$​​​组成的叠加态，各项对应的振幅为上述打印的状态左边对应的数值。 \n", "\n", "说明：\n", "\n", "（1）通过调用量子线路的`get_qs`函数，我们能够得到该量子线路在全零态基础上演化出来的量子态。\n", "\n", "（2）`get_qs`的`pr`参数代表变分量子线路中的参数值，`ket`表示是否将量子态输出为右矢形式。\n", "\n", "## 搭建Ansatz\n", "\n", "同样地，我们也可以在MindQuantum中搭建Ansatz。" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
q0: ──RX(theta0)────RY(theta1)──\n",
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\n" ], "text/plain": [ "q0: ──RX(theta0)────RY(theta1)──" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# pylint: disable=W0104\n", "ansatz = Circuit() # 初始化量子线路\n", "ansatz += RX(f'theta{0}').on(0) # RX(theta_0)门作用在第0位量子比特\n", "ansatz += RY(f'theta{1}').on(0) # RY(theta_1)门作用在第0位量子比特\n", "ansatz # 打印量子线路" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "从对Ansatz的Summary中可以看到，该量子线路由2个量子门组成，其中有2个含参量子门且参数为$\\theta_0,\\theta_1$​​，该量子线路调控的量子比特数为1。\n", "\n", "然后，对Ansatz中的参数进行赋值。由于Ansatz为需要训练的量子线路，因此Ansatz中的参数$\\theta_0$​​和$\\theta_1$​​可以随机设定，通常默认设为初始值0。我们同样可以打印此时的量子态，不过这并不是必要的步骤，只是为了再次熟悉一下`get_qs`函数。" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1¦0⟩\n" ] } ], "source": [ "theta0, theta1 = 0, 0 # 对theta0, theta1进行赋值，设为初始值0, 0\n", "state = ansatz.get_qs(pr=dict(zip(ansatz.params_name, [theta0, theta1])), ket=True)\n", "print(state)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "从上述打印可以看到，此时的状态为$|0\\rangle$​​且振幅为1。这是因为对于Ansatz来说，默认的输入量子态为$|0\\rangle$​​，而且其中的参数$\\theta_0$​​和$\\theta_1$​​都为0，此时的`RX(0)`门和`RY(0)`门都相当于`I`门，因此整个线路演化的过程就是$|0\\rangle$​​经过$I\\cdot I$，那么最后输出的态当然就是$|0\\rangle$​​​了。\n", "\n", "那么完整的量子线路就是Encoder加上Ansatz。" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
q0: ──H────RX(alpha0)────RY(alpha1)────RZ(alpha2)────RX(theta0)────RY(theta1)──\n",
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\n" ], "text/plain": [ "q0: ──H────RX(alpha0)────RY(alpha1)────RZ(alpha2)────RX(theta0)────RY(theta1)──" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# pylint: disable=W0104\n", "circuit = encoder + ansatz # 完整的量子线路由Encoder和Ansatz组成\n", "circuit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "从对完整的量子线路的Summary中可以看到，该量子线路由6个量子门组成，其中有5个含参量子门且参数为$\\alpha_0,\\alpha_1,\\alpha_2,\\theta_0,\\theta_1$​​​，该量子线路调控的量子比特数为1。\n", "\n", "## 构建哈密顿量\n", "\n", "我们对第0位量子比特执行泡利`Z`算符测量，构建对应的哈密顿量。" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-1 [Z0] \n" ] } ], "source": [ "from mindquantum.core import QubitOperator # 导入QubitOperator模块，用于构造泡利算符\n", "from mindquantum.core import Hamiltonian # 导入Hamiltonian模块，用于构建哈密顿量\n", "\n", "ham = Hamiltonian(QubitOperator('Z0', -1)) # 对第0位量子比特执行泡利Z算符测量，且将系数设置为-1，构建对应的哈密顿量\n", "print(ham)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "从上述打印可以看到，此时构建的哈密顿量为对第0位量子比特执行泡利`Z`算符测量，且系数为-1。之所以将系数设为-1，是因为在量子神经网络的训练中，Ansatz中的参数的梯度会一直下降，同时测量值也会一直减少。如果最后收敛于-1，那么此时对应的量子态是$|1\\rangle$而不是$|0\\rangle$​，如下所示\n", "\n", "$$\n", "\\langle 1|Z|1\\rangle=-1.\n", "$$\n", "\n", "而我们所希望得到的是$|0\\rangle$态。所以，将系数设为-1，那么当测量值为-1时，此时对应的量子态就是$|0\\rangle$态，如下所示\n", "\n", "$$\n", "\\langle 0|(-Z)|0\\rangle=-1.\n", "$$\n", "\n", "说明：\n", "\n", "（1）QubitOperator是作用于量子比特的算子的总和，主要用于构造泡利算符；一般格式如下：QubitOperator(term=None, coefficient=1.0)；\n", "\n", "（2）Hamiltonian是哈密顿量包装器，主要用于构建哈密顿量，一般格式如下：Hamiltonian(QubitOperator('X0 Y2', 0.5))，X0和Y2表示泡利`X`算符作用在第0位量子比特，泡利`Y`算符作用在第2位量子比特，系数为0.5。\n", "\n", "## 生成变分量子线路模拟算子\n", "\n", "对于上述搭建的量子线路，我们可以在MindQuantum生成一个变分量子线路模拟算子对其进行模拟。\n", "\n", "首先，为了方便，我们对Encoder和Ansatz中的参数数组分别命名为encoder_names和ansatz_names。" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "encoder_names = ['alpha0', 'alpha1', 'alpha2'] \n", "ansatz_names = ['theta0', 'theta1']\n" ] } ], "source": [ "encoder_names = encoder.params_name # Encoder中所有参数组成的数组，encoder.para_name系统会自动生成\n", "ansatz_names = ansatz.params_name # Ansatz中所有参数组成的数组，ansatz.para_name系统会自动生成\n", "\n", "print('encoder_names = ', encoder.params_name, '\\nansatz_names =', ansatz.params_name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "从上述打印可以看到，encoder_names为Encoder中所有参数$\\alpha_0, \\alpha_1, \\alpha_2$​组成的数组，ansatz_names为Ansatz中所有参数$\\theta_0,\\theta_1$​组成的数组，这两个数组会在生成变分量子线路模拟算子时用到。\n", "\n", "然后，我们通过`Simulator`模块得到变分量子线路演化和梯度求解的算子。" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Measurement result: [[0.29552022+0.j]]\n", "Gradient of encoder parameters: [[[0.+0.j 0.+0.j 0.+0.j]]]\n", "Gradient of ansatz parameters: [[[-0.37202556+0.j 0.87992317+0.j]]]\n" ] } ], "source": [ "# 导入Simulator模块\n", "from mindquantum.simulator import Simulator\n", "\n", "# 生成一个基于projectq后端的模拟器，并设置模拟器的比特数为量子线路的比特数。\n", "sim = Simulator('projectq', circuit.n_qubits)\n", "\n", "# 获取模拟器基于当前量子态的量子线路演化以及期望、梯度求解算子\n", "grad_ops = sim.get_expectation_with_grad(ham,\n", " circuit,\n", " encoder_params_name=encoder_names,\n", " ansatz_params_name=ansatz_names)\n", "\n", "# Encoder中的alpha0, alpha1, alpha2这三个参数组成的数组，\n", "# 将其数据类型转换为float32，并储存在encoder_data中。\n", "# MindQuantum支持多样本的batch训练，Encoder数组是两个维度，\n", "# 第一个维度为样本，第二个维度为特征（即参数）\n", "encoder_data = np.array([[alpha0, alpha1, alpha2]]).astype(np.float32)\n", "\n", "# Ansatz中的theta0, theta1这两个参数组成的数组，将其数据类型转换为float32，\n", "# 并储存在ansatzr_data中，Ansatz数据只有一个维度，特征（即参数）\n", "ansatz_data = np.array([theta0, theta1]).astype(np.float32)\n", "\n", "# 根据Encoder和Ansatz的数据，输出变分量子线路的测量值，Encoder中的参数的导数和Ansatz中的参数的导数\n", "measure_result, encoder_grad, ansatz_grad = grad_ops(encoder_data, ansatz_data)\n", "\n", "print('Measurement result: ', measure_result)\n", "print('Gradient of encoder parameters: ', encoder_grad)\n", "print('Gradient of ansatz parameters: ', ansatz_grad)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "从上述打印可以看到，测量结果（期望值）为0.29552022，Encoder中的3个参数的导数为0，0，0（因为我们对Encoder设置了no_grad()），Ansatz中的2个参数的导数为-0.37202555，-0.87992316。\n", "\n", "这里通过`get_expectation_with_grad`产生的只是一个算子，还不能进行训练，要把它放到量子神经网络里面才能进行训练。通过训练Ansatz中的参数，可以使得Ansatz中的参数的导数一直下降并接近于0，那么测量值也就会接近于-1。\n", "\n", "说明：\n", "\n", "（1）`Simulator`的`get_expectation_with_grad`用于生成变分量子线路来模拟算子，一般格式如下：\n", "\n", "```python\n", "\n", "Simulator.get_expectation_with_grad(ham,\n", " circ_right,\n", " circ_left,\n", " encoder_params_name,\n", " ansatz_params_name,\n", " parallel_worker=1)\n", "\n", "```\n", "\n", "此函数适用于计算如下模型：\n", "\n", "$$E=\\left<0\\right|U^\\dagger_l(\\theta) H U_r(\\theta)\\left|0\\right>$$\n", "\n", "其中`circ_right`是$U_r$，`circ_left`是$U_l$，当不提供时，默认跟`circ_right`是相同的线路，`encoder_params_name`指定整个体系中哪些参数是属于编码器中的参数，编码器可以将经典数据通过feature mapping映射到高位希尔伯特空间中，`ansatz_params_name`指定整个体系中哪些参数是属于待训练线路中的参数，`parallel_worker`指定并行数，当需要编码的经典数据是一个batch时，合理设置此参数可以提高计算效率。\n", "\n", "（2）MindSpore是一个全场景深度学习框架，旨在实现易开发、高效执行、全场景覆盖三大目标，提供支持异构加速的张量可微编程能力，支持云、服务器、边和端多种硬件平台。\n", "\n", "## 搭建量子神经网络" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "MQLayer<\n", " (evolution): MQOps<1 qubit projectq VQA Operator>\n", " >" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# pylint: disable=W0104\n", "from mindquantum.framework import MQLayer # 导入MQLayer\n", "import mindspore as ms # 导入mindspore\n", "\n", "ms.set_seed(1) # 设置生成随机数的种子\n", "ms.context.set_context(mode=ms.context.PYNATIVE_MODE, device_target=\"CPU\")\n", "\n", "QuantumNet = MQLayer(grad_ops)\n", "QuantumNet" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "上述打印可以看到，我们已经成功搭建了量子机器学习层，其可以无缝地跟MindSpore中其它的算子构成一张更大的机器学习网络。\n", "\n", "说明：\n", "\n", "（1）MindQuantum中的量子线路梯度计算算子都是在`PYNATIVE_MODE`下的，因此需要设置MindSpore的运行模式。\n", "\n", "（2）我们也可以通过如下代码方式搭建量子机器学习层，只是在MindQuantum中，已经将下述过程封装打包，这样我们就可以直接利用MQLayer模块搭建量子机器学习层。对于更复杂的量子-经典混合神经网络，如下搭建方式会展示它的优势。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```python\n", "\n", "class MQLayer(nn.Cell):\n", " def __init__(self, expectation_with_grad, weight='normal'):\n", " super(MQLayer, self).__init__()\n", " self.evolution = MQOps(expectation_with_grad)\n", " weight_size = len(\n", " self.evolution.expectation_with_grad.ansatz_params_name)\n", " self.weight = Parameter(initializer(weight,\n", " weight_size,\n", " dtype=ms.float32),\n", " name='ansatz_weight')\n", "\n", " def construct(self, x):\n", " return self.evolution(x, self.weight)\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 训练\n", "\n", "我们采用Adam优化器优化Ansatz中的参数。" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 : [[0.2837115]]\n", "10 : [[-0.8851233]]\n", "20 : [[-0.97001773]]\n", "30 : [[-0.9929431]]\n", "40 : [[-0.9939507]]\n", "50 : [[-0.9967015]]\n", "60 : [[-0.99878186]]\n", "70 : [[-0.9995535]]\n", "80 : [[-0.9999011]]\n", "90 : [[-0.99998033]]\n", "100 : [[-0.9999989]]\n", "110 : [[-0.99999785]]\n", "120 : [[-0.999997]]\n", "130 : [[-0.9999987]]\n", "140 : [[-0.9999998]]\n", "150 : [[-1.]]\n", "160 : [[-0.99999994]]\n", "170 : [[-1.]]\n", "180 : [[-1.]]\n", "190 : [[-1.]]\n" ] } ], "source": [ "from mindspore.nn import Adam, TrainOneStepCell # 导入Adam模块和TrainOneStepCell模块\n", "\n", "opti = Adam(QuantumNet.trainable_params(), learning_rate=0.5) # 需要优化的是Quantumnet中可训练的参数，学习率设为0.5\n", "net = TrainOneStepCell(QuantumNet, opti)\n", "\n", "for i in range(200):\n", " res = net(ms.Tensor(encoder_data))\n", " if i % 10 == 0:\n", " print(i, ': ', res)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "从上述打印可以看到，最后测量值收敛于-1。\n", "\n", "说明：\n", "\n", "（1）Adam模块通过自适应矩估计算法更新梯度，可以优化Ansazt中的参数，输入的是神经网络中可训练的参数；一般格式如下：nn.Adam(net.trainable_params(), learning_rate=0.5)；\n", "\n", "（2）TrainOneStepCell模块为网络训练包类，用优化器包装网络。生成的单元格使用输入“inputs”进行训练，将在构造函数中创建反向图，以更新参数，有不同的并行模式可用于训练。一般格式如下：nn.TrainOneStepCell(network, optimizer, sens=1.0)。\n", "\n", "## 结果呈现\n", "\n", "由于测量值已经收敛于-1，所以我们可以打印此时Ansatz中的参数。" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 2.2420275 -1.0756909]\n" ] } ], "source": [ "theta0, theta1 = QuantumNet.weight.asnumpy()\n", "\n", "print(QuantumNet.weight.asnumpy())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "从上述打印可以看到，此时Ansatz中的参数$\\theta_1, \\theta_2$分别为2.2420275和-1.0756909。\n", "\n", "通过`get_qs`，可以输出量子线路在最优参数时的量子态。" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0.37129760050057437-0.9285139157007681j)¦0⟩\n", "(1.4564552975271372e-05+6.455516706194153e-07j)¦1⟩\n" ] } ], "source": [ "pr = {'alpha0': alpha0, 'alpha1': alpha1, 'alpha2': alpha2, 'theta0': theta0, 'theta1': theta1}\n", "state = circuit.get_qs(pr=pr, ket=True)\n", "\n", "print(state)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "从上述打印可以看到，这就是量子线路在最优参数时的量子态。从其数值表示可以看到，这是一个接近于目标态$|0\\rangle$​​​的态。最后，我们计算一下此量子态与目标态$|0\\rangle$​​​​​的保真度（用于验证两个量子态的相似程度），并将保真度打印。" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.9999999997874571\n" ] } ], "source": [ "state = circuit.get_qs(pr=pr)\n", "fid = np.abs(np.vdot(state, [1, 0]))**2 # 保真度fidelity为向量内积的绝对值的模平方，即计算此时量子态对应的向量与|0>态对应的向量[1,0]的内积的模平方\n", "\n", "print(fid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "可以看到，此时的保真度为100.00%，也就是说，该状态与目标态$|0\\rangle$​​的相似程度为100.00%。\n", "\n", "综上所述，我们搭建了一个简单的量子神经网络，通过训练Ansatz中的参数，抵消了Encoder对初始量子态产生的误差，使得最后的量子态仍为$|0\\rangle$​，且保真度达到100.00%。\n", "\n", "至此，我们通过MindQuantum完成了对量子神经网络的初体验！赶紧动手体验一下量子编程的乐趣吧！\n", "\n", "若想查询更多关于MindQuantum的API，请点击：[https://mindspore.cn/mindquantum/](https://mindspore.cn/mindquantum/)。" ] } ], "metadata": { "kernelspec": { "display_name": "MindSpore", "language": "python", "name": "mindspore" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 2 }