Warm-Start Variational Quantum Policy Iteration
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913648592551936 |
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| author | Meyer, Nico Murauer, Jakob Popov, Alexander Ufrecht, Christian Plinge, Axel Mutschler, Christopher Scherer, Daniel D. |
| author_facet | Meyer, Nico Murauer, Jakob Popov, Alexander Ufrecht, Christian Plinge, Axel Mutschler, Christopher Scherer, Daniel D. |
| contents | Reinforcement learning is a powerful framework aiming to determine optimal behavior in highly complex decision-making scenarios. This objective can be achieved using policy iteration, which requires to solve a typically large linear system of equations. We propose the variational quantum policy iteration (VarQPI) algorithm, realizing this step with a NISQ-compatible quantum-enhanced subroutine. Its scalability is supported by an analysis of the structure of generic reinforcement learning environments, laying the foundation for potential quantum advantage with utility-scale quantum computers. Furthermore, we introduce the warm-start initialization variant (WS-VarQPI) that significantly reduces resource overhead. The algorithm solves a large FrozenLake environment with an underlying 256x256-dimensional linear system, indicating its practical robustness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_10546 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Warm-Start Variational Quantum Policy Iteration Meyer, Nico Murauer, Jakob Popov, Alexander Ufrecht, Christian Plinge, Axel Mutschler, Christopher Scherer, Daniel D. Quantum Physics Machine Learning Reinforcement learning is a powerful framework aiming to determine optimal behavior in highly complex decision-making scenarios. This objective can be achieved using policy iteration, which requires to solve a typically large linear system of equations. We propose the variational quantum policy iteration (VarQPI) algorithm, realizing this step with a NISQ-compatible quantum-enhanced subroutine. Its scalability is supported by an analysis of the structure of generic reinforcement learning environments, laying the foundation for potential quantum advantage with utility-scale quantum computers. Furthermore, we introduce the warm-start initialization variant (WS-VarQPI) that significantly reduces resource overhead. The algorithm solves a large FrozenLake environment with an underlying 256x256-dimensional linear system, indicating its practical robustness. |
| title | Warm-Start Variational Quantum Policy Iteration |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2404.10546 |