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 presents an overview of 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.
| Original language | English |
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| Title of host publication | 2025 IEEE International Symposium on Circuits and Systems (ISCAS) |
| Publisher | IEEE |
| Pages | 1-5 |
| Number of pages | 5 |
| ISBN (Electronic) | 9798350356830 |
| ISBN (Print) | 9798350356847 |
| DOIs | |
| Publication status | Published - 25 May 2025 |
| Event | IEEE International Symposium on Circuits and Systems, ISCAS 2025 - London, United Kingdom Duration: 25 May 2025 → 28 May 2025 https://2025.ieee-iscas.org/ (Conference Webpage) https://confcats-event-sessions.s3.us-east-1.amazonaws.com/iscas25/uploads/ISCAS_2025_Program_v24.pdf (Conference Program) https://ieeexplore.ieee.org/xpl/conhome/11043142/proceeding (Conference Proceedings) |
Publication series
| Name | IEEE International Symposium on Circuits and Systems (ISCAS) |
|---|---|
| ISSN (Print) | 0271-4302 |
| ISSN (Electronic) | 2158-1525 |
Conference
| Conference | IEEE International Symposium on Circuits and Systems, ISCAS 2025 |
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| Country/Territory | United Kingdom |
| City | London |
| Period | 25/05/25 → 28/05/25 |
| Internet address |
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User-Defined Keywords
- machine learning
- quantum computing
- quantum machine learning
- variational quantum circuits