Demonstration of machine-learning-enhanced Bayesian quantum state estimation
Machine learning (ML) has found broad applicability in quantum information science in topics as diverse as experimental design, state classification, and even studies on quantum foundations. Here, we experimentally realize an approach for defining custom prior distributions that are automatically tu...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/ace6c8 |
| _version_ | 1797748686592147456 |
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| author | Sanjaya Lohani Joseph M Lukens Atiyya A Davis Amirali Khannejad Sangita Regmi Daniel E Jones Ryan T Glasser Thomas A Searles Brian T Kirby |
| author_facet | Sanjaya Lohani Joseph M Lukens Atiyya A Davis Amirali Khannejad Sangita Regmi Daniel E Jones Ryan T Glasser Thomas A Searles Brian T Kirby |
| author_sort | Sanjaya Lohani |
| collection | DOAJ |
| description | Machine learning (ML) has found broad applicability in quantum information science in topics as diverse as experimental design, state classification, and even studies on quantum foundations. Here, we experimentally realize an approach for defining custom prior distributions that are automatically tuned using ML for Bayesian quantum state estimation methods that generally better conform to the physical properties of the underlying system than standard fixed prior distributions. Previously, researchers have looked to Bayesian quantum state tomography for advantages like uncertainty quantification, the return of reliable estimates under any measurement condition, and minimal mean-squared error. However, practical challenges related to long computation times and conceptual issues concerning how to incorporate prior knowledge most suitably can overshadow these benefits. Using both simulated and experimental measurement results, we demonstrate that ML-defined prior distributions reduce net convergence times and provide a natural way to incorporate both implicit and explicit information directly into the prior distribution. These results constitute a promising path toward practical implementations of Bayesian quantum state tomography. |
| first_indexed | 2024-03-12T16:08:22Z |
| format | Article |
| id | doaj.art-4bc83cff1a96474187361e95d2927036 |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:08:22Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-4bc83cff1a96474187361e95d29270362023-08-09T14:13:49ZengIOP PublishingNew Journal of Physics1367-26302023-01-0125808300910.1088/1367-2630/ace6c8Demonstration of machine-learning-enhanced Bayesian quantum state estimationSanjaya Lohani0https://orcid.org/0000-0003-0699-0669Joseph M Lukens1https://orcid.org/0000-0001-9650-4462Atiyya A Davis2Amirali Khannejad3Sangita Regmi4https://orcid.org/0000-0002-7857-4253Daniel E Jones5https://orcid.org/0000-0002-9854-5767Ryan T Glasser6Thomas A Searles7https://orcid.org/0000-0002-0532-7884Brian T Kirby8https://orcid.org/0000-0002-2698-9887Department of Electrical & Computer Engineering University of Illinois Chicago , Chicago, IL 60607, United States of AmericaResearch Technology Office and Quantum Collaborative, Arizona State University , Tempe, AZ 85287, United States of America; Quantum Information Science Section, Oak Ridge National Laboratory , Oak Ridge, TN 37831, United States of AmericaDepartment of Electrical & Computer Engineering University of Illinois Chicago , Chicago, IL 60607, United States of AmericaDepartment of Electrical & Computer Engineering University of Illinois Chicago , Chicago, IL 60607, United States of AmericaDepartment of Electrical & Computer Engineering University of Illinois Chicago , Chicago, IL 60607, United States of AmericaDEVCOM Army Research Laboratory , Adelphi, MD 20783, United States of AmericaTulane University , New Orleans, LA 70118, United States of AmericaDepartment of Electrical & Computer Engineering University of Illinois Chicago , Chicago, IL 60607, United States of AmericaDEVCOM Army Research Laboratory , Adelphi, MD 20783, United States of America; Tulane University , New Orleans, LA 70118, United States of AmericaMachine learning (ML) has found broad applicability in quantum information science in topics as diverse as experimental design, state classification, and even studies on quantum foundations. Here, we experimentally realize an approach for defining custom prior distributions that are automatically tuned using ML for Bayesian quantum state estimation methods that generally better conform to the physical properties of the underlying system than standard fixed prior distributions. Previously, researchers have looked to Bayesian quantum state tomography for advantages like uncertainty quantification, the return of reliable estimates under any measurement condition, and minimal mean-squared error. However, practical challenges related to long computation times and conceptual issues concerning how to incorporate prior knowledge most suitably can overshadow these benefits. Using both simulated and experimental measurement results, we demonstrate that ML-defined prior distributions reduce net convergence times and provide a natural way to incorporate both implicit and explicit information directly into the prior distribution. These results constitute a promising path toward practical implementations of Bayesian quantum state tomography.https://doi.org/10.1088/1367-2630/ace6c8quantum tomographyBayesian techniquesmachine learning |
| spellingShingle | Sanjaya Lohani Joseph M Lukens Atiyya A Davis Amirali Khannejad Sangita Regmi Daniel E Jones Ryan T Glasser Thomas A Searles Brian T Kirby Demonstration of machine-learning-enhanced Bayesian quantum state estimation New Journal of Physics quantum tomography Bayesian techniques machine learning |
| title | Demonstration of machine-learning-enhanced Bayesian quantum state estimation |
| title_full | Demonstration of machine-learning-enhanced Bayesian quantum state estimation |
| title_fullStr | Demonstration of machine-learning-enhanced Bayesian quantum state estimation |
| title_full_unstemmed | Demonstration of machine-learning-enhanced Bayesian quantum state estimation |
| title_short | Demonstration of machine-learning-enhanced Bayesian quantum state estimation |
| title_sort | demonstration of machine learning enhanced bayesian quantum state estimation |
| topic | quantum tomography Bayesian techniques machine learning |
| url | https://doi.org/10.1088/1367-2630/ace6c8 |
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