Quantum reinforcement learning in continuous action space
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 ove...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften
2025-03-01
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| Series: | Quantum |
| Online Access: | https://quantum-journal.org/papers/q-2025-03-12-1660/pdf/ |
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| _version_ | 1850037342345101312 |
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| author | Shaojun Wu Shan Jin Dingding Wen Donghong Han Xiaoting Wang |
| author_facet | Shaojun Wu Shan Jin Dingding Wen Donghong Han Xiaoting Wang |
| author_sort | Shaojun Wu |
| collection | DOAJ |
| description | 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 | Article |
| id | doaj-art-93db364a7b574abcbc37cd47260c56d3 |
| institution | DOAJ |
| issn | 2521-327X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften |
| record_format | Article |
| series | Quantum |
| spelling | doaj-art-93db364a7b574abcbc37cd47260c56d32025-08-20T02:56:54ZengVerein zur Förderung des Open Access Publizierens in den QuantenwissenschaftenQuantum2521-327X2025-03-019166010.22331/q-2025-03-12-166010.22331/q-2025-03-12-1660Quantum reinforcement learning in continuous action spaceShaojun WuShan JinDingding WenDonghong HanXiaoting WangQuantum 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.https://quantum-journal.org/papers/q-2025-03-12-1660/pdf/ |
| spellingShingle | Shaojun Wu Shan Jin Dingding Wen Donghong Han Xiaoting Wang Quantum reinforcement learning in continuous action space Quantum |
| title | Quantum reinforcement learning in continuous action space |
| title_full | Quantum reinforcement learning in continuous action space |
| title_fullStr | Quantum reinforcement learning in continuous action space |
| title_full_unstemmed | Quantum reinforcement learning in continuous action space |
| title_short | Quantum reinforcement learning in continuous action space |
| title_sort | quantum reinforcement learning in continuous action space |
| url | https://quantum-journal.org/papers/q-2025-03-12-1660/pdf/ |
| work_keys_str_mv | AT shaojunwu quantumreinforcementlearningincontinuousactionspace AT shanjin quantumreinforcementlearningincontinuousactionspace AT dingdingwen quantumreinforcementlearningincontinuousactionspace AT donghonghan quantumreinforcementlearningincontinuousactionspace AT xiaotingwang quantumreinforcementlearningincontinuousactionspace |