{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 量子启发式算法结合自动调参工具\n", "\n", "[![下载Notebook](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.7.0rc1/resource/_static/logo_notebook.svg)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/r2.7.0rc1/mindquantum/zh_cn/case_library/mindspore_qaia_automatic_parameter_adjustment.ipynb) \n", "[![下载样例代码](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.7.0rc1/resource/_static/logo_download_code.svg)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/r2.7.0rc1/mindquantum/zh_cn/case_library/mindspore_qaia_automatic_parameter_adjustment.py) \n", "[![在Gitee上查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.7.0rc1/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/r2.7.0rc1/docs/mindquantum/docs/source_zh_cn/case_library/qaia_automatic_parameter_adjustment.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 量子启发式算法\n", "\n", "量子启发式算法是一类基于**量子力学原理**的计算方法衍生或启发的经典力学方法，旨在利用量子力学的独特性质（叠加态、量子纠缠和量子并行性）来改进传统算法的性能。比较有名的是Ewin tang受HHL启发提出的算法，但目前没有实用场景。为了便于区分，我们把受量子退火或者模拟量子Ising机，称为量子退火启发式算法。研究这类算法的意义在于不断探索经典算法的上界；其次可以对现实问题进行建模，使其能够被量子算法或者量子启发式算法进行求解，并且后续可以用QPU来代替启发式算法进行加速。\n", "\n", "常见的**量子启发式算法**包括：\n", "\n", "- ASB（Adiabatic Simulated bifurcation/绝热模拟分叉算法）\n", "- BSB（Ballistic Simulated bifurcation/弹道模拟分叉算法）\n", "- DSB（Discrete Simulated bifurcation/离散模拟分叉算法）\n", "- SimCIM（Simulated Coherent Ising Machine/模拟相干伊辛机算法）\n", "- LQA（Local Quantum Annealing/局部量子退火算法）\n", "\n", "**MindQuantum**是基于昇思MindSpore开源深度学习平台开发的新一代通用量子计算框架，聚焦于NISQ阶段的算法实现与落地。结合HiQ高性能量子计算模拟器和昇思MindSpore并行自动微分能力，提供极简的开发模式和极致的性能体验。\n", "\n", "MindQuantum已经[集成量子启发式算法模块](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.11/algorithm/mindquantum.algorithm.qaia.html)，并提供**CPU、GPU、NUP/昇腾**版本，适配多种硬件设备，并提供极致性能。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[BSB/绝热模拟分叉算法](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.11/algorithm/qaia/mindquantum.algorithm.qaia.BSB.html#mindquantum.algorithm.qaia.BSB)为例，介绍量子启发式算法中参数定义：\n", "\n", "- J (Union[numpy.array, scipy.sparse.spmatrix]) - 耦合矩阵，维度为（N*N）；与求解的图、ising或qubo问题相关。\n", "- h (numpy.array) - 外场强度，维度为（N,）。\n", "- x (numpy.array) - 自旋初始化配置，维度为（N*batch_size）。会在优化过程中被修改。如果不提供（None），将被初始化为在 [-0.01, 0.01] 范围内均匀分布的随机值。默认值：None。\n", "- n_iter (int) - 迭代步数。默认值： 1000。\n", "- batch_size (int) - 样本个数。默认值： 1。\n", "- dt (float) - 迭代步长。默认值： 1。\n", "- xi (float) - 频率维数，正的常数。默认值： None。\n", "- backend (str) - 计算后端和精度：'cpu-float32'、'gpu-float16'或'gpu-int8'，默认值： 'cpu-float32'，适配CPU/GPU不同的硬件设备。\n", "\n", "可优化参数：\n", "\n", "- n_iter迭代步数表示迭代计算的步数，根据具体问题来设置，迭代步数越大，越容易收敛，求解效果越好，但是计算时间越长，可调参数。\n", "- batch_size样本个数，MindQuantum.qaia模块通过矩阵升维，提供并行化能力，样本个数越大，解的规模越大，计算时间越长，可调参数。\n", "- dt迭代步长，控制每次动力学演化的步长距离，迭代步长直接影响到算法收敛的速度和稳定性；如果dt太大，可能会导致算法发散或不稳定；如果dt太小，则算法收敛速度会较慢，可调参数。\n", "- xi频率维数，用于调整算法在频率空间上的特性，可调参数。\n", "\n", "综合考虑算法的稳定性、收敛速度、问题特性以及算法背景等因素，选择合适的参数（n_iter、batch_size、dt、xi）。\n", "\n", "通常选取一组较好的参数组合需要大规模运行实验，耗费大量时间和人力，自动化调参工具便应运而生。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 自动调参工具Optuna和Hyperopt\n", "\n", "目前业界使用最广泛的python调参工具是Optuna和Hyperopt，可以使用网格搜索等方法自动化得到目标函数的最佳结果。\n", "\n", "### Optuna\n", "\n", "Optuna 是一个开源超参数优化框架，由Preferred Networks开发，适合机器学习和深度学习，并逐步适配LLM大模型。相比传统的网格搜索Grid Search，Optuna使用贝叶斯优化等算法，能够更高效地找到最优参数组合\n", "\n", "主要特点\n", "\n", "- 大规模搜索空间，支持多种参数类型（连续、离散、分类）\n", "- 先进高效的参数搜索算法\n", "- 提供可视化和并行优化能力\n", "- 支持对接PyTorch和TensorFlow等深度学习框架\n", "\n", "常用API\n", "\n", "- Trial: 目标函数的单次调用\n", "- Study: 一次优化过程，包含一系列的 trials.\n", "- Parameter: 待优化的参数\n", "\n", "Optuna官网：" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "安装命令，通过pip安装，支持Python 3.8或更高版本\n", "\n", "```shell\n", "pip install optuna\n", "```" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[I 2025-06-16 10:21:16,981] A new study created in memory with name: no-name-bb7d4a88-3012-401d-b020-06f0b7e63c1a\n", "[I 2025-06-16 10:21:16,986] Trial 0 finished with value: 1.2332747669303585 and parameters: {'x': 0.8894709517845296}. Best is trial 0 with value: 1.2332747669303585.\n", "[I 2025-06-16 10:21:16,988] Trial 1 finished with value: 20.69100702532007 and parameters: {'x': 6.548736860417414}. Best is trial 0 with value: 1.2332747669303585.\n", "[I 2025-06-16 10:21:16,989] Trial 2 finished with value: 46.24728562544997 and parameters: {'x': 8.80053568665366}. Best is trial 0 with value: 1.2332747669303585.\n", "[I 2025-06-16 10:21:16,993] Trial 3 finished with value: 5.213333943630706 and parameters: {'x': -0.2832726389178113}. Best is trial 0 with value: 1.2332747669303585.\n", "[I 2025-06-16 10:21:16,995] Trial 4 finished with value: 85.54920023745696 and parameters: {'x': -7.249281065977882}. Best is trial 0 with value: 1.2332747669303585.\n", "[I 2025-06-16 10:21:16,995] Trial 5 finished with value: 10.244278172158554 and parameters: {'x': 5.200668394594878}. Best is trial 0 with value: 1.2332747669303585.\n", "[I 2025-06-16 10:21:16,997] Trial 6 finished with value: 31.17835152655691 and parameters: {'x': 7.5837578320121395}. Best is trial 0 with value: 1.2332747669303585.\n", "[I 2025-06-16 10:21:16,998] Trial 7 finished with value: 6.291002799247192 and parameters: {'x': -0.5081871539514733}. Best is trial 0 with value: 1.2332747669303585.\n", "[I 2025-06-16 10:21:16,999] Trial 8 finished with value: 21.702051777680936 and parameters: {'x': 6.658546101272471}. Best is trial 0 with value: 1.2332747669303585.\n", "[I 2025-06-16 10:21:17,000] Trial 9 finished with value: 60.059339007965605 and parameters: {'x': -5.7497960623467765}. Best is trial 0 with value: 1.2332747669303585.\n", "[I 2025-06-16 10:21:17,015] Trial 10 finished with value: 33.600981819914715 and parameters: {'x': -3.796635387870684}. Best is trial 0 with value: 1.2332747669303585.\n", "[I 2025-06-16 10:21:17,025] Trial 11 finished with value: 0.8505643479802889 and parameters: {'x': 1.0777395443909086}. Best is trial 11 with value: 0.8505643479802889.\n", "[I 2025-06-16 10:21:17,033] Trial 12 finished with value: 0.9492117372189054 and parameters: {'x': 2.9742749802899104}. Best is trial 11 with value: 0.8505643479802889.\n", "[I 2025-06-16 10:21:17,039] Trial 13 finished with value: 1.088651567539649 and parameters: {'x': 3.0433846690169686}. Best is trial 11 with value: 0.8505643479802889.\n", "[I 2025-06-16 10:21:17,044] Trial 14 finished with value: 1.2990109251546207 and parameters: {'x': 3.139741604555445}. Best is trial 11 with value: 0.8505643479802889.\n", "[I 2025-06-16 10:21:17,055] Trial 15 finished with value: 25.721224363705996 and parameters: {'x': -3.0716096422837986}. Best is trial 11 with value: 0.8505643479802889.\n", "[I 2025-06-16 10:21:17,062] Trial 16 finished with value: 1.421723384219888 and parameters: {'x': 3.192360425467018}. Best is trial 11 with value: 0.8505643479802889.\n", "[I 2025-06-16 10:21:17,067] Trial 17 finished with value: 131.1684799164624 and parameters: {'x': -9.452880856643118}. Best is trial 11 with value: 0.8505643479802889.\n", "[I 2025-06-16 10:21:17,078] Trial 18 finished with value: 16.982720180776216 and parameters: {'x': -2.1210096069745115}. Best is trial 11 with value: 0.8505643479802889.\n", "[I 2025-06-16 10:21:17,087] Trial 19 finished with value: 63.354308243994815 and parameters: {'x': 9.959541961946982}. Best is trial 11 with value: 0.8505643479802889.\n", "[I 2025-06-16 10:21:17,095] Trial 20 finished with value: 0.6002035355880134 and parameters: {'x': 1.2252719602415223}. Best is trial 20 with value: 0.6002035355880134.\n", "[I 2025-06-16 10:21:17,103] Trial 21 finished with value: 0.160193332718854 and parameters: {'x': 1.599758407060368}. Best is trial 21 with value: 0.160193332718854.\n", "[I 2025-06-16 10:21:17,113] Trial 22 finished with value: 0.14097107919717228 and parameters: {'x': 1.6245388446228128}. Best is trial 22 with value: 0.14097107919717228.\n", "[I 2025-06-16 10:21:17,120] Trial 23 finished with value: 13.31927509952413 and parameters: {'x': -1.6495582060742817}. Best is trial 22 with value: 0.14097107919717228.\n", "[I 2025-06-16 10:21:17,131] Trial 24 finished with value: 9.565104190348324 and parameters: {'x': 5.092750263171652}. Best is trial 22 with value: 0.14097107919717228.\n", "[I 2025-06-16 10:21:17,137] Trial 25 finished with value: 0.440523307624899 and parameters: {'x': 1.3362807011809141}. Best is trial 22 with value: 0.14097107919717228.\n", "[I 2025-06-16 10:21:17,146] Trial 26 finished with value: 0.0027116800107364622 and parameters: {'x': 1.9479262061038716}. Best is trial 26 with value: 0.0027116800107364622.\n", "[I 2025-06-16 10:21:17,153] Trial 27 finished with value: 6.359825958163664 and parameters: {'x': 4.521869536309058}. Best is trial 26 with value: 0.0027116800107364622.\n", "[I 2025-06-16 10:21:17,162] Trial 28 finished with value: 10.430425338027106 and parameters: {'x': -1.2296169026723751}. Best is trial 26 with value: 0.0027116800107364622.\n", "[I 2025-06-16 10:21:17,168] Trial 29 finished with value: 0.006421219245358646 and parameters: {'x': 2.0801325105394723}. Best is trial 26 with value: 0.0027116800107364622.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Best parameters: {'x': 1.9479262061038716}\n" ] } ], "source": [ "# 代码样例\n", "\n", "import optuna\n", "\n", "\n", "# 定义需要优化的函数objective，最小化 （x-2）^2\n", "def objective(trial):\n", " # 定义超参数x，数据类型为浮点数，范围是-10~10\n", " x = trial.suggest_float(\"x\", -10, 10)\n", " return (x - 2) ** 2\n", "\n", "\n", "# 创建study对象并调用30次\n", "study = optuna.create_study()\n", "study.optimize(objective, n_trials=30)\n", "\n", "\n", "# 打印输出最佳参数\n", "print(\"Best parameters:\", study.best_params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hyperopt\n", "\n", "Hyperopt是一个用于超参数优化的框架，由James Bergstra开发的分布式优化库，基于贝叶斯优化（Tree-structured Parzen Estimator，TPE）算法，能够高效搜索算法的最佳参数组合。\n", "\n", "主要特点：\n", "\n", "- 提供多种优化算法，随机搜索、Parzen估计器树TPE、自适应TPE等\n", "- 支持Spark和MongoDB分布式计算\n", "\n", "Hyperopt官网：" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "安装命令\n", "\n", "```shell\n", "pip install hyperopt\n", "```" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100%|██████████| 30/30 [00:00<00:00, 895.79trial/s, best loss: 0.05078371223984796]\n", "Best parameters: {'x': 1.7746475821300158}\n" ] } ], "source": [ "# 代码样例\n", "from hyperopt import fmin, tpe, hp, Trials\n", "\n", "\n", "# 定义目标函数，最小化（x-2）^2\n", "def objective(params):\n", " x = params[\"x\"]\n", " return (x - 2) ** 2\n", "\n", "\n", "# 定义搜索空间，超参数x，数据类型浮点数，是范围是-10~10\n", "space = {\"x\": hp.uniform(\"x\", -10, 10)}\n", "\n", "# 优化30次\n", "trials = Trials()\n", "best = fmin(fn=objective, space=space, algo=tpe.suggest, max_evals=30, trials=trials)\n", "\n", "print(\"Best parameters:\", best)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Optuna对比Hyperopt\n", "\n", "Optuna和Hyperopt两个自动调参框架都支持主流的贝叶斯优化等参数搜索算法。其中Optuna提供可视化功能，原生支持并行多进程，属于后起之秀；Hyperopt并行化对接MongoDB数据库，适合中小规模的参数搜索任务。\n", "\n", "综上所述，我们使用**量子启发式算法结合Optuna调参框架**，求解图最大割问题。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 最佳实践\n", "\n", "我们结合量子启发式算法和调参框架Optuna，面向GSet图计算最大割MAXCUT，找到最优参数组合。\n", "\n", "工作流：\n", "\n", "- 数据准备，此处以GSet图为例。\n", "- 数据处理，将图转化为MindQuantum.qaia启发式算法中稀疏化矩阵的格式。\n", "- 构造目标函数，定义参数搜索空间和优化项。我们以启发式算法求解最大割问题作为目标函数，选取迭代步数、频率维数、迭代步长作为超参数，并确认数据类型和范围，将每次计算出来的最大割值作为优化项。\n", "- Optuna设置优化方向（最大化）和优化次数，运行得到最佳参数组合和最大割值。" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# 导入需要的Python模块\n", "import optuna\n", "from mindquantum.algorithm.qaia import BSB\n", "import numpy as np\n", "import pandas as pd\n", "from scipy.sparse import coo_matrix" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 数据准备\n", "# 下载数据，无向图数据集来源于GSet\n", "import requests\n", "\n", "graph_file = \"https://web.stanford.edu/~yyye/yyye/Gset/G22\"\n", "\n", "# 使用requests库中的get方法发送HTTP请求，将url的响应结果存入变量，再以二进制写入模式打开文件写入本地\n", "response = requests.get(graph_file)\n", "open(\"G22\", \"wb\").write(response.content)\n", "\n", "\n", "# 如果上述-下载图集的代码执行，报错TimeoutError，说明是网络问题\n", "# 可以手动点击网址 https://web.stanford.edu/~yyye/yyye/Gset/G22，下载数据，保存在本地，与该教程同级目录" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# 数据处理\n", "def read_gset(filename, negate=True):\n", " # 读取图表\n", " graph = pd.read_csv(filename, sep=\" \")\n", " # 节点的数量\n", " n_v = int(graph.columns[0])\n", " # 边的数量\n", " n_e = int(graph.columns[1])\n", "\n", " # 如果节点和边不匹配，会抛出错误\n", " assert n_e == graph.shape[0], \"The number of edges is not matched\"\n", "\n", " # 将读取的数据转换为一个COO矩阵（Coordinate List Format），并返回一个稀疏矩阵\n", " G = coo_matrix(\n", " (\n", " np.concatenate([graph.iloc[:, -1], graph.iloc[:, -1]]),\n", " (\n", " np.concatenate([graph.iloc[:, 0] - 1, graph.iloc[:, 1] - 1]),\n", " np.concatenate([graph.iloc[:, 1] - 1, graph.iloc[:, 0] - 1]),\n", " ),\n", " ),\n", " shape=(n_v, n_v),\n", " )\n", " if negate:\n", " G = -G\n", "\n", " return G\n", "\n", "\n", "G = read_gset(\"./G22\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[I 2025-06-10 14:45:49,389] A new study created in memory with name: no-name-e39b169b-e700-46cf-b7de-0a06029b5fc2\n", "[I 2025-06-10 14:45:57,224] Trial 0 finished with value: 13150.0 and parameters: {'n_iter': 2000, 'xi': 0.9854202577397366, 'dt': 0.5477249406048266}. Best is trial 0 with value: 13150.0.\n", "[I 2025-06-10 14:46:05,089] Trial 1 finished with value: 13131.0 and parameters: {'n_iter': 2000, 'xi': 1.0144441314725534, 'dt': 0.14700420950824844}. Best is trial 0 with value: 13150.0.\n", "[I 2025-06-10 14:46:07,024] Trial 2 finished with value: 0.0 and parameters: {'n_iter': 500, 'xi': 1.0630337466081248, 'dt': 0.8816243325250461}. Best is trial 0 with value: 13150.0.\n", "[I 2025-06-10 14:46:10,916] Trial 3 finished with value: 13098.0 and parameters: {'n_iter': 1000, 'xi': 1.8987245320688324, 'dt': 0.48426104373507317}. Best is trial 0 with value: 13150.0.\n", "[I 2025-06-10 14:46:11,696] Trial 4 finished with value: 0.0 and parameters: {'n_iter': 200, 'xi': 0.5409946559755382, 'dt': 1.8674038859144357}. Best is trial 0 with value: 13150.0.\n", "[I 2025-06-10 14:46:17,508] Trial 5 finished with value: 13131.0 and parameters: {'n_iter': 1500, 'xi': 1.85980870314362, 'dt': 0.4736079017307153}. Best is trial 0 with value: 13150.0.\n", "[I 2025-06-10 14:46:18,312] Trial 6 finished with value: 13149.0 and parameters: {'n_iter': 200, 'xi': 0.8832047795920951, 'dt': 0.2944176599505819}. Best is trial 0 with value: 13150.0.\n", "[I 2025-06-10 14:46:26,011] Trial 7 finished with value: 0.0 and parameters: {'n_iter': 2000, 'xi': 1.415239274236914, 'dt': 1.2270769369519832}. Best is trial 0 with value: 13150.0.\n", "[I 2025-06-10 14:46:33,920] Trial 8 finished with value: 13209.0 and parameters: {'n_iter': 2000, 'xi': 0.6685516703951494, 'dt': 0.38186215999423023}. Best is trial 8 with value: 13209.0.\n", "[I 2025-06-10 14:46:34,700] Trial 9 finished with value: 0.0 and parameters: {'n_iter': 200, 'xi': 1.0212129276196251, 'dt': 1.1399747781193834}. Best is trial 8 with value: 13209.0.\n", "[I 2025-06-10 14:46:36,649] Trial 10 finished with value: 0.0 and parameters: {'n_iter': 500, 'xi': 0.2674806227804169, 'dt': 1.5826105377506663}. Best is trial 8 with value: 13209.0.\n", "[I 2025-06-10 14:46:44,426] Trial 11 finished with value: 13187.0 and parameters: {'n_iter': 2000, 'xi': 0.6201525299807226, 'dt': 0.7386461621543139}. Best is trial 8 with value: 13209.0.\n", "[I 2025-06-10 14:46:52,221] Trial 12 finished with value: 13241.0 and parameters: {'n_iter': 2000, 'xi': 0.5000901811624008, 'dt': 0.7695917543118803}. Best is trial 12 with value: 13241.0.\n", "[I 2025-06-10 14:47:00,805] Trial 13 finished with value: 13287.0 and parameters: {'n_iter': 2000, 'xi': 0.24501427912089346, 'dt': 0.09730316419264518}. Best is trial 13 with value: 13287.0.\n", "[I 2025-06-10 14:47:06,599] Trial 14 finished with value: 0.0 and parameters: {'n_iter': 1500, 'xi': 0.1343794192379361, 'dt': 1.443147898698505}. Best is trial 13 with value: 13287.0.\n", "[I 2025-06-10 14:47:10,795] Trial 15 finished with value: 13186.0 and parameters: {'n_iter': 1000, 'xi': 0.35354754708959213, 'dt': 0.048648627578946946}. Best is trial 13 with value: 13287.0.\n", "[I 2025-06-10 14:47:18,560] Trial 16 finished with value: 13223.0 and parameters: {'n_iter': 2000, 'xi': 0.41501193595639063, 'dt': 0.8710262789265769}. Best is trial 13 with value: 13287.0.\n", "[I 2025-06-10 14:47:27,510] Trial 17 finished with value: 13320.0 and parameters: {'n_iter': 2000, 'xi': 0.10962746454226069, 'dt': 0.04327730177800215}. Best is trial 17 with value: 13320.0.\n", "[I 2025-06-10 14:47:36,277] Trial 18 finished with value: 13319.0 and parameters: {'n_iter': 2000, 'xi': 0.20492428842592136, 'dt': 0.07675851169706162}. Best is trial 17 with value: 13320.0.\n", "[I 2025-06-10 14:47:40,353] Trial 19 finished with value: 13092.0 and parameters: {'n_iter': 1000, 'xi': 1.4449821018638078, 'dt': 0.01891161997759272}. Best is trial 17 with value: 13320.0.\n", "[I 2025-06-10 14:47:46,951] Trial 20 finished with value: 13336.0 and parameters: {'n_iter': 1500, 'xi': 0.1032352341871752, 'dt': 0.2749199047871625}. Best is trial 20 with value: 13336.0.\n", "[I 2025-06-10 14:47:53,580] Trial 21 finished with value: 13333.0 and parameters: {'n_iter': 1500, 'xi': 0.18933056261923595, 'dt': 0.2690572699319539}. Best is trial 20 with value: 13336.0.\n", "[I 2025-06-10 14:48:00,416] Trial 22 finished with value: 13350.0 and parameters: {'n_iter': 1500, 'xi': 0.12643971691997044, 'dt': 0.2427101496435709}. Best is trial 22 with value: 13350.0.\n", "[I 2025-06-10 14:48:06,374] Trial 23 finished with value: 13170.0 and parameters: {'n_iter': 1500, 'xi': 0.768187969451931, 'dt': 0.27833172283830976}. Best is trial 22 with value: 13350.0.\n", "[I 2025-06-10 14:48:12,467] Trial 24 finished with value: 13285.0 and parameters: {'n_iter': 1500, 'xi': 0.37712716123693546, 'dt': 0.6503970389856277}. Best is trial 22 with value: 13350.0.\n", "[I 2025-06-10 14:48:19,216] Trial 25 finished with value: 13333.0 and parameters: {'n_iter': 1500, 'xi': 0.11711321639360335, 'dt': 0.25135181974448084}. Best is trial 22 with value: 13350.0.\n", "[I 2025-06-10 14:48:25,025] Trial 26 finished with value: 13113.0 and parameters: {'n_iter': 1500, 'xi': 1.3106221351956426, 'dt': 0.6122590372769572}. Best is trial 22 with value: 13350.0.\n", "[I 2025-06-10 14:48:31,207] Trial 27 finished with value: 13296.0 and parameters: {'n_iter': 1500, 'xi': 0.314056428843962, 'dt': 0.39524343565408737}. Best is trial 22 with value: 13350.0.\n", "[I 2025-06-10 14:48:37,222] Trial 28 finished with value: 13226.0 and parameters: {'n_iter': 1500, 'xi': 0.4811504744246722, 'dt': 0.23460200349669913}. Best is trial 22 with value: 13350.0.\n", "[I 2025-06-10 14:48:43,207] Trial 29 finished with value: 13201.0 and parameters: {'n_iter': 1500, 'xi': 0.7292919802986543, 'dt': 0.5383674375183389}. Best is trial 22 with value: 13350.0.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Best parameters: {'n_iter': 1500, 'xi': 0.12643971691997044, 'dt': 0.2427101496435709}\n", "Best cut: 13350.0\n" ] } ], "source": [ "# 优化目标函数\n", "# 将待优化的算法构造成目标函数，此处以模拟分岔BSB为例，定义输入的参数n_iter\\xi\\dt，返回值cut作为优化项\n", "def objective(trial):\n", " # 定义参数搜索空间，迭代步数，整数类型，可选值[200, 500, 1000, 1500, 2000]\n", " n_iter = trial.suggest_categorical(\"n_iter\", [200, 500, 1000, 1500, 2000])\n", " # 设置频率维数，浮点数，范围是0.1~2\n", " xi = trial.suggest_float(\"xi\", 0.1, 2)\n", " # 设置迭代步长，浮点数，范围是0~2\n", " dt = trial.suggest_float(\"dt\", 0, 2)\n", "\n", " # 使用BSB算法计算图的最大割值\n", " bsb = BSB(J=G, batch_size=100, n_iter=n_iter, xi=xi, dt=dt)\n", " bsb.update()\n", " cut_value = bsb.calc_cut()\n", "\n", " max_cut = max(cut_value)\n", " return max_cut\n", "\n", "\n", "# 设置优化方向为最大化\n", "study = optuna.create_study(direction=\"maximize\")\n", "# 优化30次\n", "study.optimize(objective, n_trials=30)\n", "\n", "# 打印出Optuna调参后得到的最近参数和结果\n", "print(\"Best parameters:\", study.best_params)\n", "print(\"Best cut:\", study.best_value)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "上述样例代码执行30次后，得到最佳参数组合，最大割值为13350\n", "\n", "> 注意，本篇教程只是提供样例代码，优化次数仅为30，调参工具的结果具有一定随机性；在实际应用中结合业务需求可适当放大参数范围。" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", " \n", " \n", "\n", "\n", "
SoftwareVersion
mindquantum0.10.0
scipy1.11.3
numpy1.26.1
SystemInfo
Python3.10.13
OSLinux x86_64
Memory810.22 GB
CPU Max Thread96
DateTue Jun 10 14:49:51 2025
\n" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from mindquantum.utils.show_info import InfoTable\n", "\n", "InfoTable(\"mindquantum\", \"scipy\", \"numpy\")" ] } ], "metadata": { "kernelspec": { "display_name": "base", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.3" } }, "nbformat": 4, "nbformat_minor": 4 }