Quantum Machine Learning (QML) holds significant promise for solving computational challenges across diverse domains. However, its practical deployment is constrained by the limitations of noisy intermediate-scale quantum (NISQ) devices, including noise, limited scalability, and trainability issues in variational quantum circuits (VQCs). We introduce the multi-chip ensemble VQC framework, which partitions high-dimensional computations across smaller quantum chips to enhance scalability, trainability, and noise resilience. We show that this approach mitigates barren plateaus, reduces quantum error bias and variance, and maintains robust generalization through controlled entanglement. Designed to align with current and emerging quantum hardware, the framework demonstrates strong potential for enabling scalable QML on near-term devices, as validated by experiments on standard benchmark datasets (MNIST, FashionMNIST, CIFAR-10) and real world dataset (PhysioNet EEG).

翻译：量子机器学习（QML）在解决跨领域计算难题方面展现出巨大潜力。然而，其实践部署受到噪声中等规模量子（NISQ）设备固有局限的制约，包括噪声干扰、有限的可扩展性以及变分量子电路（VQC）中的可训练性问题。本文提出多芯片集成VQC框架，通过将高维计算任务分配到多个小型量子芯片上执行，从而提升系统的可扩展性、可训练性与噪声鲁棒性。研究表明，该方法能有效缓解梯度消失平台现象，降低量子误差的偏差与方差，并通过受控纠缠机制保持稳健的泛化能力。该框架专为适配当前及新兴量子硬件设计，在标准基准数据集（MNIST、FashionMNIST、CIFAR-10）和真实世界数据集（PhysioNet EEG）上的实验验证表明，其具备在近期量子设备上实现可扩展QML的显著潜力。