Learning and forecasting open quantum dynamics with correlated noise
Abstract The development of practical quantum processors relies on the ability to control and predict their functioning despite the presence of noise. This is particularly challenging for temporarily correlated noise. Here we propose a physics-inspired supervised machine learning approach to efficie...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Communications Physics |
| Online Access: | https://doi.org/10.1038/s42005-025-01944-2 |
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| _version_ | 1832594710703636480 |
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| author | Xinfang Zhang Zhihao Wu Gregory A. L. White Zhongcheng Xiang Shun Hu Zhihui Peng Yong Liu Dongning Zheng Xiang Fu Anqi Huang Dario Poletti Kavan Modi Junjie Wu Mingtang Deng Chu Guo |
| author_facet | Xinfang Zhang Zhihao Wu Gregory A. L. White Zhongcheng Xiang Shun Hu Zhihui Peng Yong Liu Dongning Zheng Xiang Fu Anqi Huang Dario Poletti Kavan Modi Junjie Wu Mingtang Deng Chu Guo |
| author_sort | Xinfang Zhang |
| collection | DOAJ |
| description | Abstract The development of practical quantum processors relies on the ability to control and predict their functioning despite the presence of noise. This is particularly challenging for temporarily correlated noise. Here we propose a physics-inspired supervised machine learning approach to efficiently and accurately predict the functioning of quantum processors in the presence of correlated noise, which only requires data from randomized benchmarking experiments. To demonstrate the efficacy of our technique, we analyze the data from a superconducting quantum processor with tunable correlated noise. We produce training data by evolving the system for a number of time steps, and with this, we fully quantify the correlated noise and accurately predict the dynamics of the system for times beyond the training data. This approach shows a path towards efficient and effective learning of noisy quantum dynamics and optimally control quantum processors over long and complex computations even in the presence of correlated noise. |
| format | Article |
| id | doaj-art-a12abee9cbfc4eb9bcb701a70cf9df6c |
| institution | Kabale University |
| issn | 2399-3650 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Physics |
| spelling | doaj-art-a12abee9cbfc4eb9bcb701a70cf9df6c2025-01-19T12:26:25ZengNature PortfolioCommunications Physics2399-36502025-01-018111010.1038/s42005-025-01944-2Learning and forecasting open quantum dynamics with correlated noiseXinfang Zhang0Zhihao Wu1Gregory A. L. White2Zhongcheng Xiang3Shun Hu4Zhihui Peng5Yong Liu6Dongning Zheng7Xiang Fu8Anqi Huang9Dario Poletti10Kavan Modi11Junjie Wu12Mingtang Deng13Chu Guo14Institute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense TechnologyInstitute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense TechnologySchool of Physics and Astronomy, Monash UniversityInstitute of Physics, Chinese Academy of SciencesInstitute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense TechnologyKey Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education, Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications, Hunan Normal UniversityInstitute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense TechnologyInstitute of Physics, Chinese Academy of SciencesInstitute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense TechnologyInstitute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense TechnologyScience, Mathematics and Technology Cluster and Engineering Product Development Pillar, Singapore University of Technology and DesignSchool of Physics and Astronomy, Monash UniversityInstitute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense TechnologyInstitute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense TechnologyKey Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education, Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications, Hunan Normal UniversityAbstract The development of practical quantum processors relies on the ability to control and predict their functioning despite the presence of noise. This is particularly challenging for temporarily correlated noise. Here we propose a physics-inspired supervised machine learning approach to efficiently and accurately predict the functioning of quantum processors in the presence of correlated noise, which only requires data from randomized benchmarking experiments. To demonstrate the efficacy of our technique, we analyze the data from a superconducting quantum processor with tunable correlated noise. We produce training data by evolving the system for a number of time steps, and with this, we fully quantify the correlated noise and accurately predict the dynamics of the system for times beyond the training data. This approach shows a path towards efficient and effective learning of noisy quantum dynamics and optimally control quantum processors over long and complex computations even in the presence of correlated noise.https://doi.org/10.1038/s42005-025-01944-2 |
| spellingShingle | Xinfang Zhang Zhihao Wu Gregory A. L. White Zhongcheng Xiang Shun Hu Zhihui Peng Yong Liu Dongning Zheng Xiang Fu Anqi Huang Dario Poletti Kavan Modi Junjie Wu Mingtang Deng Chu Guo Learning and forecasting open quantum dynamics with correlated noise Communications Physics |
| title | Learning and forecasting open quantum dynamics with correlated noise |
| title_full | Learning and forecasting open quantum dynamics with correlated noise |
| title_fullStr | Learning and forecasting open quantum dynamics with correlated noise |
| title_full_unstemmed | Learning and forecasting open quantum dynamics with correlated noise |
| title_short | Learning and forecasting open quantum dynamics with correlated noise |
| title_sort | learning and forecasting open quantum dynamics with correlated noise |
| url | https://doi.org/10.1038/s42005-025-01944-2 |
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