量子电路生成神经网络权重，实现高效抗噪的混合机器学习。
VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning
- 用量子电路生成经典神经网络的初始权重，训练时混合、推理时纯经典。
- 在量子点与基因序列数据上准确率超基线，参数量减少且抗噪声更强。
- 理论证明表达能力随量子深度指数提升，适合资源受限的实用场景。
变分量子电路（VQCs）在量子机器学习中具有潜力，但面临表达能力弱、可训练性差和抗噪声能力不足的问题。本文提出VQC-MLPNet，一种新型混合架构：在训练过程中，量子电路生成经典多层感知机（MLP）的第一层权重，而推理全程由经典计算完成。该设计保持了可扩展性，降低量子资源消耗，支持实际部署。基于统计学习与神经正切核理论的分析，建立了明确的风险界，证明其表达能力和可训练性优于纯量子或现有混合方法。理论表明，表示容量相对于量子电路深度和量子比特数呈指数级提升，显著优于独立量子电路及现有混合架构。在多种数据集上的实证结果，包括量子点分类与基因组序列分析，显示VQC-MLPNet在真实噪声模型下仍保持高准确率和鲁棒性，性能超越经典与量子基线，同时使用更少的可训练参数。
原文摘要 · Abstract (English)
Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience. We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically. This design preserves scalability, reduces quantum resource demands, and enables practical deployment. We provide a theoretical analysis based on statistical learning and neural tangent kernel theory, establishing explicit risk bounds and demonstrating improved expressivity and trainability compared to purely quantum or existing hybrid approaches. These theoretical insights demonstrate exponential improvements in representation capacity relative to quantum circuit depth and the number of qubits, providing clear computational advantages over standalone quantum circuits and existing hybrid quantum architectures. Empirical results on diverse datasets, including quantum-dot classification and genomic sequence analysis, show that VQC-MLPNet achieves high accuracy and robustness under realistic noise models, outperforming classical and quantum baselines while using significantly fewer trainable parameters.
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