用预训练神经网络提升量子电路的优化与泛化能力
Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits
- 用预训练神经网络指导量子电路参数优化
- 在量子点分类任务中显著提升表现
- 适合量子机器学习初学者与硬件受限研究者
量子机器学习（QML）潜力巨大，但受制于量子比特数量。本文提出一种新方法，利用预训练神经网络增强变分量子电路（VQC），有效分离近似误差与量子比特数量的依赖关系，并消除对严格条件的依赖，使QML更适用于实际应用。该方法显著改善了VQC的参数优化，提升了表示与泛化能力，经理论分析和大量实验证实，尤其在量子点分类任务中表现优异。结果还可推广至人类基因组分析等场景，展现出广泛应用前景。本工作突破现有量子硬件限制，为量子计算在机器学习、材料科学、医学等领域的深入应用开辟新路径。
原文摘要 · Abstract (English)
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.
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