Fast State Stabilization using Deep Reinforcement Learning for Measurement-based Quantum Feedback Control

[2408.11328] Fast State Stabilization using Deep Reinforcement Learning for Measurement-based Quantum Feedback Control Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Systems and Control arXiv:2408.11328 (eess) [Submitted on 21 Aug 2024 ( v1 ), last revised 20 Jan 2025 (this version, v2)] Title: Fast State Stabilization using Deep Reinforcement Learning for Measurement-based Quantum Feedback Control Authors: Chunxiang Song , Yanan Liu , Daoyi Dong , Hidehiro Yonezawa View a PDF of the paper titled Fast State Stabilization using Deep Reinforcement Learning for Measurement-based Quantum Feedback Control, by Chunxiang Song and 3 other authors View PDF HTML (experimental) Abstract: The stabilization of quantum states is a fundamental problem for realizing various quantum technologies. Measurement-based-feedback strategies have demonstrated powerful performance, and the construction of quantum control signals using measurement information has attracted great interest. However, the interaction between quantum systems and the environment is inevitable, especially when measurements are introduced, which leads to decoherence. To mitigate decoherence, it is desirable to stabilize quantum systems faster, thereby reducing the time of interaction with the environment. In this paper, we utilize information obtained from measurement and apply deep reinforcement learning (DRL) algorithms, without explicitly constructing specific complex measurement-control mappings, to rapidly drive random initial quantum state to the target state. The proposed DRL algorithm has the ability to speed up the convergence to a target state, which shortens the interaction between quantum systems and their environments to protect coherence. Simulations are performed on two-qubit and three-qubit systems, and the results show that our algorithm can successfully stabilize random initial quantum system to the target entangled state, with a convergence time faster than traditional methods such as Lyapunov feedback control and several DRL algorithms with different reward functions. Moreover, it exhibits robustness against imperfect measurements and delays in system evolution. Subjects: Systems and Control (eess.SY) Cite as: arXiv:2408.11328 [eess.SY] (or arXiv:2408.11328v2 [eess.SY] for this version) https://doi.org/10.48550/arXiv.2408.11328 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Published in IEEE Transactions on Quantum Engineering, 2025 Related DOI : https://doi.org/10.1109/TQE.2025.3606123 Focus to learn more DOI(s) linking to related resources Submission history From: Chunxiang Song [ view email ] [v1] Wed, 21 Aug 2024 04:21:30 UTC (7,042 KB) [v2] Mon, 20 Jan 2025 07:30:21 UTC (7,338 KB) Full-text links: Access Paper: View a PDF of the paper titled Fast State Stabilization using Deep Reinforcement Learning for Measurement-based Quantum Feedback Control, by Chunxiang Song and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.SY < prev | next > new | recent | 2024-08 Change to browse by: cs cs.SY eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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