探索量子计算与机器学习的交叉融合，推动技术革新。
Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning
- 用变分量子电路构建适用于噪声中等规模量子设备的模型架构。
- 结合理论与实证研究，揭示量子机器学习的新方向。
- 适合对量子计算与人工智能交叉领域感兴趣的科研人员。
量子机器学习（QML）是量子计算与传统机器学习相结合的快速发展的领域，旨在通过量子力学的独特能力变革机器学习，并利用机器学习技术推进量子计算研究。本文介绍了面向机器学习范式的量子计算，其中采用变分量子电路（VQC）在噪声中等规模量子（NISQ）设备上构建QML架构。我们还探讨了面向量子计算范式的机器学习，展示近期的理论与实证成果。特别地，深入讨论了未来研究方向，探索了QML研究可能带来的产业影响。
原文摘要 · Abstract (English)
Quantum machine learning (QML) is a rapidly growing field that combines quantum computing principles with traditional machine learning. It seeks to revolutionize machine learning by harnessing the unique capabilities of quantum mechanics and employs machine learning techniques to advance quantum computing research. This paper introduces quantum computing for the machine learning paradigm, where variational quantum circuits (VQC) are used to develop QML architectures on noisy intermediate-scale quantum (NISQ) devices. We discuss machine learning for the quantum computing paradigm, showcasing our recent theoretical and empirical findings. In particular, we delve into future directions for studying QML, exploring the potential industrial impacts of QML research.
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