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Measurement-Based Feedback Quantum Control with Deep Reinforcement Learning for a Double-Well Nonlinear Potential
https://oist.repo.nii.ac.jp/records/2372
https://oist.repo.nii.ac.jp/records/2372fb9e04bb-0ed0-4812-b2c3-0054871faa52
名前 / ファイル | ライセンス | アクション |
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Borah-2021-Measurement-Based Feedback Quantum_1 (475.3 kB)
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CC BY 4.0
Creative Commons Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/) |
Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2021-11-18 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | Measurement-Based Feedback Quantum Control with Deep Reinforcement Learning for a Double-Well Nonlinear Potential | |||||
言語 | ||||||
言語 | eng | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | journal article | |||||
著者(英) |
Borah, Sangkha
× Borah, Sangkha× Sarma, Bijita× Kewming, Michael× Milburn, Gerard J.× Twamley, Jason |
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書誌情報 |
en : Physical Review Letters 巻 127, 号 19, p. 190403, 発行日 2021-11-02 |
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抄録 | ||||||
内容記述タイプ | Other | |||||
内容記述 | Closed loop quantum control uses measurement to control the dynamics of a quantum system to achieve either a desired target state or target dynamics. In the case when the quantum Hamiltonian is quadratic in x and p, there are known optimal control techniques to drive the dynamics toward particular states, e.g., the ground state. However, for nonlinear Hamiltonian such control techniques often fail. We apply deep reinforcement learning (DRL), where an artificial neural agent explores and learns to control the quantum evolution of a highly nonlinear system (double well), driving the system toward the ground state with high fidelity. We consider a DRL strategy which is particularly motivated by experiment where the quantum system is continuously but weakly measured. This measurement is then fed back to the neural agent and used for training. We show that the DRL can effectively learn counterintuitive strategies to cool the system to a nearly pure “cat” state, which has a high overlap fidelity with the true ground state. | |||||
出版者 | ||||||
出版者 | American Physical Society | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 0031-9007 | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 1079-7114 | |||||
DOI | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | DOI | |||||
関連識別子 | info:doi/10.1103/PhysRevLett.127.190403 | |||||
権利 | ||||||
権利情報 | © 2021 American Physical Society | |||||
関連サイト | ||||||
識別子タイプ | URI | |||||
関連識別子 | https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.127.190403 | |||||
著者版フラグ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 |