Quantum Reinforcement Learning: Recent Advances and Future Directions
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866914096418390016 |
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| author | Kaldari, Jawaher Tariq, Shehbaz Al-Kuwari, Saif Chen, Samuel Yen-Chi Chatzinotas, Symeon Shin, Hyundong |
| author_facet | Kaldari, Jawaher Tariq, Shehbaz Al-Kuwari, Saif Chen, Samuel Yen-Chi Chatzinotas, Symeon Shin, Hyundong |
| contents | As quantum machine learning continues to evolve, reinforcement learning stands out as a particularly promising yet underexplored frontier. In this survey, we investigate the recent advances in QRL to assess its potential in various applications. While QRL has generally received less attention than other quantum machine learning approaches, recent research reveals its distinct advantages and transversal applicability in both quantum and classical domains. We present a comprehensive analysis of the QRL framework, including its algorithms, architectures, and supporting SDK, as well as its applications in diverse fields. Additionally, we discuss the challenges and opportunities that QRL can unfold, highlighting promising use cases that may drive innovation in quantum-inspired reinforcement learning and catalyze its adoption in various interdisciplinary contexts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14595 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Reinforcement Learning: Recent Advances and Future Directions Kaldari, Jawaher Tariq, Shehbaz Al-Kuwari, Saif Chen, Samuel Yen-Chi Chatzinotas, Symeon Shin, Hyundong Quantum Physics As quantum machine learning continues to evolve, reinforcement learning stands out as a particularly promising yet underexplored frontier. In this survey, we investigate the recent advances in QRL to assess its potential in various applications. While QRL has generally received less attention than other quantum machine learning approaches, recent research reveals its distinct advantages and transversal applicability in both quantum and classical domains. We present a comprehensive analysis of the QRL framework, including its algorithms, architectures, and supporting SDK, as well as its applications in diverse fields. Additionally, we discuss the challenges and opportunities that QRL can unfold, highlighting promising use cases that may drive innovation in quantum-inspired reinforcement learning and catalyze its adoption in various interdisciplinary contexts. |
| title | Quantum Reinforcement Learning: Recent Advances and Future Directions |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2510.14595 |