用图神经网络优化量子电路，自动找更简单且准确的量子模型。
Graph-Based Bayesian Optimization for Quantum Circuit Architecture Search with Uncertainty Calibrated Surrogates
- 用图结构表示电路，结合不确定性校准的代理模型进行搜索
- 在网络安全数据集上，找到复杂度更低、精度更高的量子电路
- 支持噪声鲁棒性测试，适合需要可解释性的量子算法研究者
量子电路设计是实际量子机器学习应用的关键瓶颈。本文提出一种自动化框架，利用基于图的贝叶斯优化与图神经网络（GNN）代理模型，发现并优化变分量子电路（VQCs）。电路以图形式表示，通过蒙特卡洛丢弃驱动的期望改进获取函数进行变异与选择。候选电路在下一代防火墙遥测与物联网（NF-ToN-IoT-V2）网络安全数据集上经特征选择与缩放后，由混合量子-经典变分分类器评估。该方法在复杂度更低的前提下，分类精度优于或等同于基于MLP的代理、随机搜索和贪心GNN选择。通过标准量子噪声通道（包括幅度阻尼、相位阻尼、热弛豫、去极化及读出比特翻转）评估了鲁棒性。实现完全可复现，包含时间基准测试与最优电路导出，为自动化量子电路发现提供可扩展且可解释的路径。
原文摘要 · Abstract (English)
Quantum circuit design is a key bottleneck for practical quantum machine learning on complex, real-world data. We present an automated framework that discovers and refines variational quantum circuits (VQCs) using graph-based Bayesian optimization with a graph neural network (GNN) surrogate. Circuits are represented as graphs and mutated and selected via an expected improvement acquisition function informed by surrogate uncertainty with Monte Carlo dropout. Candidate circuits are evaluated with a hybrid quantum-classical variational classifier on the next generation firewall telemetry and network internet of things (NF-ToN-IoT-V2) cybersecurity dataset, after feature selection and scaling for quantum embedding. We benchmark our pipeline against an MLP-based surrogate, random search, and greedy GNN selection. The GNN-guided optimizer consistently finds circuits with lower complexity and competitive or superior classification accuracy compared to all baselines. Robustness is assessed via a noise study across standard quantum noise channels, including amplitude damping, phase damping, thermal relaxation, depolarizing, and readout bit flip noise. The implementation is fully reproducible, with time benchmarking and export of best found circuits, providing a scalable and interpretable route to automated quantum circuit discovery.
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