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.

翻译：量子机器学习（QML）展现出巨大潜力，但目前受限于可用量子比特数量。本文提出一种创新方法，利用预训练神经网络增强变分量子电路（VQC）。该技术有效分离了近似误差与量子比特数量之间的关联，并消除了对限制性条件的需求，从而提升了QML在实际应用中的可行性。通过严格的理论分析和在量子点分类任务上的大量实证测试，证明我们的方法显著改善了VQC的参数优化过程，同时在表征能力和泛化性能方面取得了显著提升。此外，我们的研究成果可扩展至人类基因组分析等应用领域，证明了该方法的广泛适用性。通过突破当前量子硬件的限制，本工作为新一代先进QML应用开辟了道路，充分释放了量子计算在机器学习、材料科学、医学、仿生学及众多交叉学科领域的潜力。