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.

翻译：实用量子机器学习（QML）在当前硬件上面临噪声、可扩展性有限以及变分量子电路（VQC）可训练性差等挑战。我们提出了一种多芯片集成VQC框架，系统性地克服了这些障碍。通过将高维计算任务划分到多个独立运行的较小量子芯片组成的集成系统中，并利用受控的芯片间纠缠边界，我们的方法显著缓解了训练中的贫瘠高原现象，提升了泛化能力，并独特地同时降低了量子误差的偏差与方差，且无需额外的误差缓解开销。这使得大规模数据的鲁棒处理成为可能，这一点在标准基准数据集（MNIST、FashionMNIST、CIFAR-10）以及真实的PhysioNet EEG数据集上得到了验证。该框架与新兴的模块化量子硬件发展趋势相契合，为更具可扩展性的QML铺平了道路。