Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits

TL;DR

This paper proposes a novel method that combines pre-trained neural networks with variational quantum circuits to improve quantum machine learning, addressing hardware limitations and enhancing performance across multiple applications.

Contribution

It introduces a new approach that leverages pre-trained neural networks to enhance VQCs, improving optimization and generalization without hardware restrictions.

Findings

Significant improvement in parameter optimization for VQCs

Enhanced representation and generalization capabilities

Successful application to quantum dot classification and genome analysis

Abstract

Quantum Machine Learning (QML) offers tremendous potential but is currently limited by the availability of qubits. We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC). This technique effectively separates approximation error from qubit count and removes the need for restrictive conditions, making QML more viable for real-world applications. Our method significantly improves parameter optimization for VQC while delivering notable gains in representation and generalization capabilities, as evidenced by rigorous theoretical analysis and extensive empirical testing on quantum dot classification tasks. Moreover, our results extend to applications such as human genome analysis, demonstrating the broad applicability of our approach. By addressing the constraints of current quantum hardware, our work paves the way for a new era of advanced QML applications, unlocking the full potential of quantum computing in fields such as machine learning, materials science, medicine, mimetics, and various interdisciplinary areas.