Quantum reinforcement learning in continuous action space
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2020
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866912280627642368 |
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| author | Wu, Shaojun Jin, Shan Wen, Dingding Han, Donghong Wang, Xiaoting |
| author_facet | Wu, Shaojun Jin, Shan Wen, Dingding Han, Donghong Wang, Xiaoting |
| contents | Quantum reinforcement learning (QRL) is a promising paradigm for near-term quantum devices. While existing QRL methods have shown success in discrete action spaces, extending these techniques to continuous domains is challenging due to the curse of dimensionality introduced by discretization. To overcome this limitation, we introduce a quantum Deep Deterministic Policy Gradient (DDPG) algorithm that efficiently addresses both classical and quantum sequential decision problems in continuous action spaces. Moreover, our approach facilitates single-shot quantum state generation: a one-time optimization produces a model that outputs the control sequence required to drive a fixed initial state to any desired target state. In contrast, conventional quantum control methods demand separate optimization for each target state. We demonstrate the effectiveness of our method through simulations and discuss its potential applications in quantum control. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2012_10711 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Quantum reinforcement learning in continuous action space Wu, Shaojun Jin, Shan Wen, Dingding Han, Donghong Wang, Xiaoting Quantum Physics Machine Learning Quantum reinforcement learning (QRL) is a promising paradigm for near-term quantum devices. While existing QRL methods have shown success in discrete action spaces, extending these techniques to continuous domains is challenging due to the curse of dimensionality introduced by discretization. To overcome this limitation, we introduce a quantum Deep Deterministic Policy Gradient (DDPG) algorithm that efficiently addresses both classical and quantum sequential decision problems in continuous action spaces. Moreover, our approach facilitates single-shot quantum state generation: a one-time optimization produces a model that outputs the control sequence required to drive a fixed initial state to any desired target state. In contrast, conventional quantum control methods demand separate optimization for each target state. We demonstrate the effectiveness of our method through simulations and discuss its potential applications in quantum control. |
| title | Quantum reinforcement learning in continuous action space |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2012.10711 |