Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensembles

TL;DR

This paper introduces a multi-chip ensemble framework for quantum machine learning that addresses noise, scalability, and trainability issues by partitioning computations across multiple quantum chips, improving performance on standard benchmarks and real-world data.

Contribution

It proposes a novel multi-chip ensemble VQC approach that mitigates barren plateaus and reduces quantum errors without extra overhead, enabling scalable quantum machine learning.

Findings

Mitigates barren plateaus in VQCs

Enhances generalization and robustness

Successfully processes large-scale data on benchmarks

Abstract

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.