Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909388192612352 |
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| author | Qi, Jun Yang, Chao-Han Chen, Samuel Yen-Chi Chen, Pin-Yu Zenil, Hector Tegner, Jesper |
| author_facet | Qi, Jun Yang, Chao-Han Chen, Samuel Yen-Chi Chen, Pin-Yu Zenil, Hector Tegner, Jesper |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08552 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits Qi, Jun Yang, Chao-Han Chen, Samuel Yen-Chi Chen, Pin-Yu Zenil, Hector Tegner, Jesper Machine Learning Artificial Intelligence Quantum Physics 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. |
| title | Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits |
| topic | Machine Learning Artificial Intelligence Quantum Physics |
| url | https://arxiv.org/abs/2411.08552 |