通过跳过测量瓶颈提升量子机器学习性能
Readout-Side Bypass for Residual Hybrid Quantum-Classical Models
- 在量子-经典接口添加残差连接，直接融合原始输入与量子特征
- 联邦设置下准确率比纯量子模型最高提升55%
- 适合资源受限、注重隐私的边缘计算场景
量子机器学习（QML）虽具紧凑且表达力强的特性，但受限于量子到经典读出的狭窄瓶颈，导致性能受限并放大隐私风险。本文提出一种轻量级残差混合架构，在分类前将量子特征与原始输入拼接，绕过该瓶颈且不增加量子复杂度。实验表明，该模型在集中式与联邦设置下均优于纯量子及先前混合模型，相比量子基线最高提升55%准确率，同时保持低通信开销和更强隐私鲁棒性。消融实验验证了量子-经典接口残差连接的有效性。本方法为将量子模型融入隐私敏感、资源受限的联邦边缘学习等近中期场景提供了实用路径。
原文摘要 · Abstract (English)
Quantum machine learning (QML) promises compact and expressive representations, but suffers from the measurement bottleneck - a narrow quantum-to-classical readout that limits performance and amplifies privacy risk. We propose a lightweight residual hybrid architecture that concatenates quantum features with raw inputs before classification, bypassing the bottleneck without increasing quantum complexity. Experiments show our model outperforms pure quantum and prior hybrid models in both centralized and federated settings. It achieves up to +55% accuracy improvement over quantum baselines, while retaining low communication cost and enhanced privacy robustness. Ablation studies confirm the effectiveness of the residual connection at the quantum-classical interface. Our method offers a practical, near-term pathway for integrating quantum models into privacy-sensitive, resource-constrained settings like federated edge learning.
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