Performance optimization of quantum Otto cycle via deep reinforcement learning

LI Jiansong; LI Hai; YU Wenli; HAO Yaming

doi:10.3969/j.issn.1007-5461.2025.01.007

Journals >Chinese Journal of Quantum Electronics >Volume 42 >Issue 1 >Page 70 > Article

Chinese Journal of Quantum Electronics
Vol. 42, Issue 1, 70 (2025)

Performance optimization of quantum Otto cycle via deep reinforcement learning

LI Jiansong^1,*, LI Hai¹, YU Wenli², and HAO Yaming¹

Author Affiliations

¹School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai 264005, China

²School of Computer Science and Technology, Shandong Technology and Business University, Yantai 264005, China

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DOI: 10.3969/j.issn.1007-5461.2025.01.007 Cite this Article

Jiansong LI, Hai LI, Wenli YU, Yaming HAO. Performance optimization of quantum Otto cycle via deep reinforcement learning[J]. Chinese Journal of Quantum Electronics, 2025, 42(1): 70 Copy Citation Text

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Fig. 1. Diagram of the ideal QOC

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Fig. 2. Diagram of optimization of the additional driving field by reinforcement learning

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Fig. 3. Comparison of energy cost and output work in the optimization additional driving and free evolution schemes.(a) Energy cost

〈C_{t o t}〉

and irreversible work

〈W_{i r r}〉

; (b) Output work

W_{e d}^{t o t}

and

W_{f r e e}^{t o t}

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Fig. 4. Comparison of performance of QOC of the optimization additional driving and free evolution schemes.(a) Power

P_{e d}

and

P_{f r e e}

; (b) Efficiency

η_{e d}

and

η_{f r e e}

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Fig. 5. Optimization distribution of additional driving field with

τ = 18

. (a)

f_{e d}^{e x p}

in expansion process; (b)

f_{e d}^{c o m p}

in compression process

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Jiansong LI, Hai LI, Wenli YU, Yaming HAO. Performance optimization of quantum Otto cycle via deep reinforcement learning[J]. Chinese Journal of Quantum Electronics, 2025, 42(1): 70

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