Controlling nonergodicity in quantum many-body systems by reinforcement learning Article Swipe
Lili Ye
,
Ying‐Cheng Lai
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1103/physrevresearch.7.013256
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1103/physrevresearch.7.013256
Finding optimal control strategies to suppress quantum thermalization for arbitrarily initial states, the so-called quantum nonergodicity control, is important for quantum information science and technologies. Previous control methods relied largely on theoretical model of the target quantum system, but invertible model approximations and inaccuracies can lead to control failures. We develop a model-free and deep reinforcement-learning (DRL) framework for quantum nonergodicity control. It is a machine-learning method with the unique focus on balancing exploration and exploitation strategies to maximize the cumulative rewards so as to preserve the initial memory in the time-dependent nonergodic metrics over a long stretch of time. We use the paradigmatic one-dimensional tilted Fermi-Hubbard system to demonstrate that the DRL agent can efficiently learn the quantum many-body system solely through the interactions with the environment. The optimal policy obtained by the DRL provides broader control scenarios for managing nonergodicity in the phase diagram as compared to, e.g., the specific protocol for Wannier-Stark localization. The continuous control protocols and observations are experimentally feasible. The model-free nature of DRL and its versatile search space for control functions render promising nonergodicity control in more complex quantum many-body systems.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1103/physrevresearch.7.013256
- OA Status
- gold
- Cited By
- 2
- References
- 102
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4408288652Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1103/physrevresearch.7.013256Digital Object Identifier
- Title
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Controlling nonergodicity in quantum many-body systems by reinforcement learningWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-03-10Full publication date if available
- Authors
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Lili Ye, Ying‐Cheng LaiList of authors in order
- Landing page
-
https://doi.org/10.1103/physrevresearch.7.013256Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1103/physrevresearch.7.013256Direct OA link when available
- Concepts
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Reinforcement learning, Psychology, Quantum, Reinforcement, Cognitive science, Cognitive psychology, Computer science, Artificial intelligence, Social psychology, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
- References (count)
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102Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.in | 89, 142, 182 |
| abstract_inverted_index.is | 17, 63 |
| abstract_inverted_index.of | 33, 98, 168 |
| abstract_inverted_index.on | 30, 71 |
| abstract_inverted_index.so | 82 |
| abstract_inverted_index.to | 4, 46, 77, 84, 108 |
| abstract_inverted_index.DRL | 112, 134, 169 |
| abstract_inverted_index.The | 128, 156, 165 |
| abstract_inverted_index.and | 23, 42, 53, 74, 160, 170 |
| abstract_inverted_index.are | 162 |
| abstract_inverted_index.but | 38 |
| abstract_inverted_index.can | 44, 114 |
| abstract_inverted_index.for | 8, 19, 58, 139, 153, 175 |
| abstract_inverted_index.its | 171 |
| abstract_inverted_index.the | 12, 34, 68, 79, 86, 90, 102, 111, 117, 123, 126, 133, 143, 150 |
| abstract_inverted_index.to, | 148 |
| abstract_inverted_index.use | 101 |
| abstract_inverted_index.deep | 54 |
| abstract_inverted_index.lead | 45 |
| abstract_inverted_index.long | 96 |
| abstract_inverted_index.more | 183 |
| abstract_inverted_index.over | 94 |
| abstract_inverted_index.that | 110 |
| abstract_inverted_index.with | 67, 125 |
| abstract_inverted_index.(DRL) | 56 |
| abstract_inverted_index.agent | 113 |
| abstract_inverted_index.e.g., | 149 |
| abstract_inverted_index.focus | 70 |
| abstract_inverted_index.learn | 116 |
| abstract_inverted_index.model | 32, 40 |
| abstract_inverted_index.phase | 144 |
| abstract_inverted_index.space | 174 |
| abstract_inverted_index.time. | 99 |
| abstract_inverted_index.memory | 88 |
| abstract_inverted_index.method | 66 |
| abstract_inverted_index.nature | 167 |
| abstract_inverted_index.policy | 130 |
| abstract_inverted_index.relied | 28 |
| abstract_inverted_index.render | 178 |
| abstract_inverted_index.search | 173 |
| abstract_inverted_index.solely | 121 |
| abstract_inverted_index.system | 107, 120 |
| abstract_inverted_index.target | 35 |
| abstract_inverted_index.tilted | 105 |
| abstract_inverted_index.unique | 69 |
| abstract_inverted_index.Finding | 0 |
| abstract_inverted_index.broader | 136 |
| abstract_inverted_index.complex | 184 |
| abstract_inverted_index.control | 2, 26, 47, 137, 158, 176, 181 |
| abstract_inverted_index.develop | 50 |
| abstract_inverted_index.diagram | 145 |
| abstract_inverted_index.initial | 10, 87 |
| abstract_inverted_index.largely | 29 |
| abstract_inverted_index.methods | 27 |
| abstract_inverted_index.metrics | 93 |
| abstract_inverted_index.optimal | 1, 129 |
| abstract_inverted_index.quantum | 6, 14, 20, 36, 59, 118, 185 |
| abstract_inverted_index.rewards | 81 |
| abstract_inverted_index.science | 22 |
| abstract_inverted_index.states, | 11 |
| abstract_inverted_index.stretch | 97 |
| abstract_inverted_index.system, | 37 |
| abstract_inverted_index.through | 122 |
| abstract_inverted_index.Previous | 25 |
| abstract_inverted_index.compared | 147 |
| abstract_inverted_index.control, | 16 |
| abstract_inverted_index.control. | 61 |
| abstract_inverted_index.managing | 140 |
| abstract_inverted_index.maximize | 78 |
| abstract_inverted_index.obtained | 131 |
| abstract_inverted_index.preserve | 85 |
| abstract_inverted_index.protocol | 152 |
| abstract_inverted_index.provides | 135 |
| abstract_inverted_index.specific | 151 |
| abstract_inverted_index.suppress | 5 |
| abstract_inverted_index.systems. | 187 |
| abstract_inverted_index.balancing | 72 |
| abstract_inverted_index.failures. | 48 |
| abstract_inverted_index.feasible. | 164 |
| abstract_inverted_index.framework | 57 |
| abstract_inverted_index.functions | 177 |
| abstract_inverted_index.important | 18 |
| abstract_inverted_index.many-body | 119, 186 |
| abstract_inverted_index.promising | 179 |
| abstract_inverted_index.protocols | 159 |
| abstract_inverted_index.scenarios | 138 |
| abstract_inverted_index.so-called | 13 |
| abstract_inverted_index.versatile | 172 |
| abstract_inverted_index.continuous | 157 |
| abstract_inverted_index.cumulative | 80 |
| abstract_inverted_index.invertible | 39 |
| abstract_inverted_index.model-free | 52, 166 |
| abstract_inverted_index.nonergodic | 92 |
| abstract_inverted_index.strategies | 3, 76 |
| abstract_inverted_index.arbitrarily | 9 |
| abstract_inverted_index.demonstrate | 109 |
| abstract_inverted_index.efficiently | 115 |
| abstract_inverted_index.exploration | 73 |
| abstract_inverted_index.information | 21 |
| abstract_inverted_index.theoretical | 31 |
| abstract_inverted_index.environment. | 127 |
| abstract_inverted_index.exploitation | 75 |
| abstract_inverted_index.inaccuracies | 43 |
| abstract_inverted_index.interactions | 124 |
| abstract_inverted_index.observations | 161 |
| abstract_inverted_index.paradigmatic | 103 |
| abstract_inverted_index.Fermi-Hubbard | 106 |
| abstract_inverted_index.Wannier-Stark | 154 |
| abstract_inverted_index.localization. | 155 |
| abstract_inverted_index.nonergodicity | 15, 60, 141, 180 |
| abstract_inverted_index.technologies. | 24 |
| abstract_inverted_index.approximations | 41 |
| abstract_inverted_index.experimentally | 163 |
| abstract_inverted_index.thermalization | 7 |
| abstract_inverted_index.time-dependent | 91 |
| abstract_inverted_index.one-dimensional | 104 |
| abstract_inverted_index.machine-learning | 65 |
| abstract_inverted_index.reinforcement-learning | 55 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.91525297 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |