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.

翻译：量子机器学习（QML）是一个快速发展的领域，它将量子计算原理与传统机器学习相结合。该领域旨在通过利用量子力学的独特能力来革新机器学习，并运用机器学习技术推动量子计算研究。本文介绍了面向机器学习范式的量子计算，其中变分量子电路（VQC）被用于在含噪声中等规模量子（NISQ）设备上构建QML架构。我们探讨了面向量子计算范式的机器学习，展示了我们近期的理论与实证研究成果。特别地，我们深入分析了QML研究的未来方向，并探讨了QML研究可能带来的产业影响。