Abstract
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.
Abstract (translated)
量子电路设计是复杂现实世界数据上实用的量子机器学习的关键瓶颈。我们提出了一种自动化的框架，该框架使用基于图的贝叶斯优化和图神经网络（GNN）代理来发现并精炼变分量子电路（VQCs）。电路以图形表示，并通过预期改进获取函数进行变异和选择，该函数根据蒙特卡洛 dropout 确定的替代不确定性进行信息更新。候选电路在下一代防火墙遥测和网络物联网（NF-ToN-IoT-V2）网络安全数据集上使用混合量子经典变分分类器评估，在特征选择和量子嵌入缩放之后。 我们通过基于多层感知机（MLP）的代理、随机搜索和贪婪 GNN 选择与我们的管道进行基准测试。由 GNN 指导的优化器在所有基线中，始终找到复杂度较低且分类准确率相当或更优的电路。通过对包括幅度衰减、相位衰减、热松弛、去极化以及读出比特翻转噪声在内的标准量子噪声通道进行噪音研究来评估其鲁棒性。 该实现完全可重复，并提供时间基准测试和最佳发现电路的导出，为自动量子电路发现提供了可扩展且解释性强的方法。
URL
https://arxiv.org/abs/2512.09586
