Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning
This foundational work addresses the problem of advancing both quantum computing and machine learning for researchers and industries, though it is incremental as it builds on existing paradigms.
The paper tackles the integration of quantum computing and machine learning by introducing variational quantum circuits on NISQ devices to develop QML architectures, and it explores future directions and potential industrial impacts of this research.
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.