Challenges for Reinforcement Learning in Quantum Circuit Design
Fuente:
arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
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| _version_ | 1866909281221083136 |
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| author | Altmann, Philipp Stein, Jonas Kölle, Michael Bärligea, Adelina Gabor, Thomas Phan, Thomy Feld, Sebastian Linnhoff-Popien, Claudia |
| author_facet | Altmann, Philipp Stein, Jonas Kölle, Michael Bärligea, Adelina Gabor, Thomas Phan, Thomy Feld, Sebastian Linnhoff-Popien, Claudia |
| contents | Quantum computing (QC) in the current NISQ era is still limited in size and precision. Hybrid applications mitigating those shortcomings are prevalent to gain early insight and advantages. Hybrid quantum machine learning (QML) comprises both the application of QC to improve machine learning (ML) and ML to improve QC architectures. This work considers the latter, leveraging reinforcement learning (RL) to improve quantum circuit design (QCD), which we formalize by a set of generic objectives. Furthermore, we propose qcd-gym, a concrete framework formalized as a Markov decision process, to enable learning policies capable of controlling a universal set of continuously parameterized quantum gates. Finally, we provide benchmark comparisons to assess the shortcomings and strengths of current state-of-the-art RL algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_11337 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Challenges for Reinforcement Learning in Quantum Circuit Design Altmann, Philipp Stein, Jonas Kölle, Michael Bärligea, Adelina Gabor, Thomas Phan, Thomy Feld, Sebastian Linnhoff-Popien, Claudia Quantum Physics Machine Learning Quantum computing (QC) in the current NISQ era is still limited in size and precision. Hybrid applications mitigating those shortcomings are prevalent to gain early insight and advantages. Hybrid quantum machine learning (QML) comprises both the application of QC to improve machine learning (ML) and ML to improve QC architectures. This work considers the latter, leveraging reinforcement learning (RL) to improve quantum circuit design (QCD), which we formalize by a set of generic objectives. Furthermore, we propose qcd-gym, a concrete framework formalized as a Markov decision process, to enable learning policies capable of controlling a universal set of continuously parameterized quantum gates. Finally, we provide benchmark comparisons to assess the shortcomings and strengths of current state-of-the-art RL algorithms. |
| title | Challenges for Reinforcement Learning in Quantum Circuit Design |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2312.11337 |