用多芯片协同解决量子机器学习的噪声与可扩展性难题
Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensembles
- 将高维计算分到多个小型量子芯片上并控制芯片间纠缠
- 在多个数据集上实现误差偏差与方差同步降低
- 适合追求可扩展量子机器学习的科研与工程团队
实际量子机器学习（QML）面临当前硬件上变分量子电路（VQC）的噪声、可扩展性差和训练困难问题。本文提出一种多芯片集成式VQC框架，通过将高维计算任务分配给多个独立运行的小型量子芯片，并引入受控的芯片间纠缠边界，系统性克服上述挑战。该方法显著缓解了平庸梯度问题，提升了泛化能力，并首次在无需额外纠错开销的情况下，同时降低量子误差的偏差与方差。该框架在标准基准数据集（MNIST、FashionMNIST、CIFAR-10）及真实世界PhysioNet EEG数据集上验证有效，契合新兴模块化量子硬件发展趋势，为更可扩展的量子机器学习提供可行路径。
原文摘要 · Abstract (English)
Practical Quantum Machine Learning (QML) is challenged by noise, limited scalability, and poor trainability in Variational Quantum Circuits (VQCs) on current hardware. We propose a multi-chip ensemble VQC framework that systematically overcomes these hurdles. By partitioning high-dimensional computations across ensembles of smaller, independently operating quantum chips and leveraging controlled inter-chip entanglement boundaries, our approach demonstrably mitigates barren plateaus, enhances generalization, and uniquely reduces both quantum error bias and variance simultaneously without additional mitigation overhead. This allows for robust processing of large-scale data, as validated on standard benchmarks (MNIST, FashionMNIST, CIFAR-10) and a real-world PhysioNet EEG dataset, aligning with emerging modular quantum hardware and paving the way for more scalable QML.
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