Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning

• URL: http://arxiv.org/abs/2411.09403v1
• Date: Thu, 14 Nov 2024 12:27:50 GMT
• Title: Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning
• Authors: Jun Qi, Chao-Han Yang, Samuel Yen-Chi Chen, Pin-Yu Chen,
• Abstract summary: Quantum machine learning (QML) is a rapidly growing field that combines quantum computing principles with traditional machine learning. This paper introduces quantum computing for the machine learning paradigm, where variational quantum circuits are used to develop QML architectures.
• Score: 54.80832749095356
• License:
• Abstract: 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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