VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning

TL;DR

VQC-MLPNet introduces a hybrid quantum-classical neural network architecture that enhances expressivity, trainability, and noise resilience, with theoretical guarantees and superior empirical performance on complex datasets.

Contribution

It proposes a novel hybrid architecture where a variational quantum circuit generates first-layer weights for a classical MLP, improving scalability and resource efficiency.

Findings

Achieves exponential improvements in representation capacity.

Demonstrates high accuracy and robustness on diverse datasets.

Outperforms classical and quantum baselines with fewer parameters.

Abstract

Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience. We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically. This design preserves scalability, reduces quantum resource demands, and enables practical deployment. We provide a theoretical analysis based on statistical learning and neural tangent kernel theory, establishing explicit risk bounds and demonstrating improved expressivity and trainability compared to purely quantum or existing hybrid approaches. These theoretical insights demonstrate exponential improvements in representation capacity relative to quantum circuit depth and the number of qubits, providing clear computational advantages over standalone quantum circuits and existing hybrid quantum architectures. Empirical results on diverse datasets, including quantum-dot classification and genomic sequence analysis, show that VQC-MLPNet achieves high accuracy and robustness under realistic noise models, outperforming classical and quantum baselines while using significantly fewer trainable parameters.