Introduction to Quantum Machine Learning and Quantum Architecture Search
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909588778909696 |
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| author | Chen, Samuel Yen-Chi Liang, Zhiding |
| author_facet | Chen, Samuel Yen-Chi Liang, Zhiding |
| contents | Recent advancements in quantum computing (QC) and machine learning (ML) have fueled significant research efforts aimed at integrating these two transformative technologies. Quantum machine learning (QML), an emerging interdisciplinary field, leverages quantum principles to enhance the performance of ML algorithms. Concurrently, the exploration of systematic and automated approaches for designing high-performance quantum circuit architectures for QML tasks has gained prominence, as these methods empower researchers outside the quantum computing domain to effectively utilize quantum-enhanced tools. This tutorial will provide an in-depth overview of recent breakthroughs in both areas, highlighting their potential to expand the application landscape of QML across diverse fields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_16131 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Introduction to Quantum Machine Learning and Quantum Architecture Search Chen, Samuel Yen-Chi Liang, Zhiding Quantum Physics Artificial Intelligence Emerging Technologies Machine Learning Neural and Evolutionary Computing Recent advancements in quantum computing (QC) and machine learning (ML) have fueled significant research efforts aimed at integrating these two transformative technologies. Quantum machine learning (QML), an emerging interdisciplinary field, leverages quantum principles to enhance the performance of ML algorithms. Concurrently, the exploration of systematic and automated approaches for designing high-performance quantum circuit architectures for QML tasks has gained prominence, as these methods empower researchers outside the quantum computing domain to effectively utilize quantum-enhanced tools. This tutorial will provide an in-depth overview of recent breakthroughs in both areas, highlighting their potential to expand the application landscape of QML across diverse fields. |
| title | Introduction to Quantum Machine Learning and Quantum Architecture Search |
| topic | Quantum Physics Artificial Intelligence Emerging Technologies Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2504.16131 |