Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 49 references that are in the map. First posted on arXiv 13 Nov 2024.
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21 of 49 references.
References · 21
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9 more
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