Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 30 of its 46 references that are in the map. First posted on arXiv 14 Nov 2024.
Builds on
30 of 46 references.
References · 30
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18 more
- 2021Variational Quantum Reinforcement Learning via Evolutionary Optimizationcited by 50
- 2021An end-to-end trainable hybrid classical-quantum classifiercited by 49
- 2022When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computingcited by 40
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.
- 2026Hybrid Quantum-Classical Neural Architecture Search18 May 2026
- 2025Quantum Machine Unlearning: Foundations, Mechanisms, and Taxonomy1 Nov 2025