Meta-learning assisted robust control of universal quantum gates with uncertainties
Abstract Achieving high-fidelity quantum gates is crucial for reliable quantum computing. However, decoherence and control pulse imperfections pose significant challenges in realizing the theoretical fidelity of quantum gates in practical systems. To address these challenges, we propose the meta-rei...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | npj Quantum Information |
| Online Access: | https://doi.org/10.1038/s41534-025-01034-9 |
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| _version_ | 1849325858884419584 |
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| author | Shihui Zhang Zibo Miao Yu Pan Sibo Tao Yu Chen |
| author_facet | Shihui Zhang Zibo Miao Yu Pan Sibo Tao Yu Chen |
| author_sort | Shihui Zhang |
| collection | DOAJ |
| description | Abstract Achieving high-fidelity quantum gates is crucial for reliable quantum computing. However, decoherence and control pulse imperfections pose significant challenges in realizing the theoretical fidelity of quantum gates in practical systems. To address these challenges, we propose the meta-reinforcement learning quantum control algorithm (metaQctrl), which leverages a two-layer learning framework to enhance robustness and fidelity. The inner reinforcement learning network focuses on decision making for specific optimization problems, while the outer meta-learning network adapts to varying environments and provides feedback to the inner network. Our comparative analysis regarding the realization of universal quantum gates demonstrates that metaQctrl achieves higher fidelity with fewer control pulses than conventional methods in the presence of uncertainties. These results can contribute to the exploration of the quantum speed limit and facilitate the implementation of quantum circuits with system imperfections involved. |
| format | Article |
| id | doaj-art-0805611fdeee4c7b85dafb3b94d0ee89 |
| institution | Kabale University |
| issn | 2056-6387 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Quantum Information |
| spelling | doaj-art-0805611fdeee4c7b85dafb3b94d0ee892025-08-20T03:48:18ZengNature Portfolionpj Quantum Information2056-63872025-05-0111111010.1038/s41534-025-01034-9Meta-learning assisted robust control of universal quantum gates with uncertaintiesShihui Zhang0Zibo Miao1Yu Pan2Sibo Tao3Yu Chen4School of Intelligence Science and Engineering, Harbin Institute of TechnologySchool of Intelligence Science and Engineering, Harbin Institute of TechnologyCollege of Control Science and Engineering, Zhejiang UniversitySchool of Mathematical Sciences, Fudan UniversityMechanical and Automation Engineering, The Chinese University of Hong KongAbstract Achieving high-fidelity quantum gates is crucial for reliable quantum computing. However, decoherence and control pulse imperfections pose significant challenges in realizing the theoretical fidelity of quantum gates in practical systems. To address these challenges, we propose the meta-reinforcement learning quantum control algorithm (metaQctrl), which leverages a two-layer learning framework to enhance robustness and fidelity. The inner reinforcement learning network focuses on decision making for specific optimization problems, while the outer meta-learning network adapts to varying environments and provides feedback to the inner network. Our comparative analysis regarding the realization of universal quantum gates demonstrates that metaQctrl achieves higher fidelity with fewer control pulses than conventional methods in the presence of uncertainties. These results can contribute to the exploration of the quantum speed limit and facilitate the implementation of quantum circuits with system imperfections involved.https://doi.org/10.1038/s41534-025-01034-9 |
| spellingShingle | Shihui Zhang Zibo Miao Yu Pan Sibo Tao Yu Chen Meta-learning assisted robust control of universal quantum gates with uncertainties npj Quantum Information |
| title | Meta-learning assisted robust control of universal quantum gates with uncertainties |
| title_full | Meta-learning assisted robust control of universal quantum gates with uncertainties |
| title_fullStr | Meta-learning assisted robust control of universal quantum gates with uncertainties |
| title_full_unstemmed | Meta-learning assisted robust control of universal quantum gates with uncertainties |
| title_short | Meta-learning assisted robust control of universal quantum gates with uncertainties |
| title_sort | meta learning assisted robust control of universal quantum gates with uncertainties |
| url | https://doi.org/10.1038/s41534-025-01034-9 |
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