Dynamical simulation via quantum machine learning with provable generalization
Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated. Here we develop a framework for using QML methods to...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2024-03-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.6.013241 |
| _version_ | 1797210249771352064 |
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| author | Joe Gibbs Zoë Holmes Matthias C. Caro Nicholas Ezzell Hsin-Yuan Huang Lukasz Cincio Andrew T. Sornborger Patrick J. Coles |
| author_facet | Joe Gibbs Zoë Holmes Matthias C. Caro Nicholas Ezzell Hsin-Yuan Huang Lukasz Cincio Andrew T. Sornborger Patrick J. Coles |
| author_sort | Joe Gibbs |
| collection | DOAJ |
| description | Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated. Here we develop a framework for using QML methods to simulate quantum dynamics on near-term quantum hardware. We use generalization bounds, which bound the error a machine learning model makes on unseen data, to rigorously analyze the training data requirements of an algorithm within this framework. Our algorithm is thus resource efficient in terms of qubit and data requirements. Furthermore, our preliminary numerics for the XY model exhibit efficient scaling with problem size, and we simulate 20 times longer than Trotterization on IBMQ-Bogota. |
| first_indexed | 2024-04-24T10:07:36Z |
| format | Article |
| id | doaj.art-a995f3ee90b94a3db452aa089bbc0cbc |
| institution | Directory Open Access Journal |
| issn | 2643-1564 |
| language | English |
| last_indexed | 2024-04-24T10:07:36Z |
| publishDate | 2024-03-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj.art-a995f3ee90b94a3db452aa089bbc0cbc2024-04-12T17:40:02ZengAmerican Physical SocietyPhysical Review Research2643-15642024-03-016101324110.1103/PhysRevResearch.6.013241Dynamical simulation via quantum machine learning with provable generalizationJoe GibbsZoë HolmesMatthias C. CaroNicholas EzzellHsin-Yuan HuangLukasz CincioAndrew T. SornborgerPatrick J. ColesMuch attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated. Here we develop a framework for using QML methods to simulate quantum dynamics on near-term quantum hardware. We use generalization bounds, which bound the error a machine learning model makes on unseen data, to rigorously analyze the training data requirements of an algorithm within this framework. Our algorithm is thus resource efficient in terms of qubit and data requirements. Furthermore, our preliminary numerics for the XY model exhibit efficient scaling with problem size, and we simulate 20 times longer than Trotterization on IBMQ-Bogota.http://doi.org/10.1103/PhysRevResearch.6.013241 |
| spellingShingle | Joe Gibbs Zoë Holmes Matthias C. Caro Nicholas Ezzell Hsin-Yuan Huang Lukasz Cincio Andrew T. Sornborger Patrick J. Coles Dynamical simulation via quantum machine learning with provable generalization Physical Review Research |
| title | Dynamical simulation via quantum machine learning with provable generalization |
| title_full | Dynamical simulation via quantum machine learning with provable generalization |
| title_fullStr | Dynamical simulation via quantum machine learning with provable generalization |
| title_full_unstemmed | Dynamical simulation via quantum machine learning with provable generalization |
| title_short | Dynamical simulation via quantum machine learning with provable generalization |
| title_sort | dynamical simulation via quantum machine learning with provable generalization |
| url | http://doi.org/10.1103/PhysRevResearch.6.013241 |
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