Improved machine learning algorithm for predicting ground state properties
Abstract Finding the ground state of a quantum many-body system is a fundamental problem in quantum physics. In this work, we give a classical machine learning (ML) algorithm for predicting ground state properties with an inductive bias encoding geometric locality. The proposed ML model can efficien...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-01-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-45014-7 |
| _version_ | 1797274038809133056 |
|---|---|
| author | Laura Lewis Hsin-Yuan Huang Viet T. Tran Sebastian Lehner Richard Kueng John Preskill |
| author_facet | Laura Lewis Hsin-Yuan Huang Viet T. Tran Sebastian Lehner Richard Kueng John Preskill |
| author_sort | Laura Lewis |
| collection | DOAJ |
| description | Abstract Finding the ground state of a quantum many-body system is a fundamental problem in quantum physics. In this work, we give a classical machine learning (ML) algorithm for predicting ground state properties with an inductive bias encoding geometric locality. The proposed ML model can efficiently predict ground state properties of an n-qubit gapped local Hamiltonian after learning from only $${{{{{{{\mathcal{O}}}}}}}}(\log (n))$$ O ( log ( n ) ) data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require $${{{{{{{\mathcal{O}}}}}}}}({n}^{c})$$ O ( n c ) data for a large constant c. Furthermore, the training and prediction time of the proposed ML model scale as $${{{{{{{\mathcal{O}}}}}}}}(n\log n)$$ O ( n log n ) in the number of qubits n. Numerical experiments on physical systems with up to 45 qubits confirm the favorable scaling in predicting ground state properties using a small training dataset. |
| first_indexed | 2024-03-07T14:52:40Z |
| format | Article |
| id | doaj.art-87019dd60c254c2fab03bb0955c59007 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-07T14:52:40Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-87019dd60c254c2fab03bb0955c590072024-03-05T19:36:15ZengNature PortfolioNature Communications2041-17232024-01-011511810.1038/s41467-024-45014-7Improved machine learning algorithm for predicting ground state propertiesLaura Lewis0Hsin-Yuan Huang1Viet T. Tran2Sebastian Lehner3Richard Kueng4John Preskill5California Institute of TechnologyCalifornia Institute of TechnologyJohannes Kepler UniversityJohannes Kepler UniversityJohannes Kepler UniversityCalifornia Institute of TechnologyAbstract Finding the ground state of a quantum many-body system is a fundamental problem in quantum physics. In this work, we give a classical machine learning (ML) algorithm for predicting ground state properties with an inductive bias encoding geometric locality. The proposed ML model can efficiently predict ground state properties of an n-qubit gapped local Hamiltonian after learning from only $${{{{{{{\mathcal{O}}}}}}}}(\log (n))$$ O ( log ( n ) ) data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require $${{{{{{{\mathcal{O}}}}}}}}({n}^{c})$$ O ( n c ) data for a large constant c. Furthermore, the training and prediction time of the proposed ML model scale as $${{{{{{{\mathcal{O}}}}}}}}(n\log n)$$ O ( n log n ) in the number of qubits n. Numerical experiments on physical systems with up to 45 qubits confirm the favorable scaling in predicting ground state properties using a small training dataset.https://doi.org/10.1038/s41467-024-45014-7 |
| spellingShingle | Laura Lewis Hsin-Yuan Huang Viet T. Tran Sebastian Lehner Richard Kueng John Preskill Improved machine learning algorithm for predicting ground state properties Nature Communications |
| title | Improved machine learning algorithm for predicting ground state properties |
| title_full | Improved machine learning algorithm for predicting ground state properties |
| title_fullStr | Improved machine learning algorithm for predicting ground state properties |
| title_full_unstemmed | Improved machine learning algorithm for predicting ground state properties |
| title_short | Improved machine learning algorithm for predicting ground state properties |
| title_sort | improved machine learning algorithm for predicting ground state properties |
| url | https://doi.org/10.1038/s41467-024-45014-7 |
| work_keys_str_mv | AT lauralewis improvedmachinelearningalgorithmforpredictinggroundstateproperties AT hsinyuanhuang improvedmachinelearningalgorithmforpredictinggroundstateproperties AT vietttran improvedmachinelearningalgorithmforpredictinggroundstateproperties AT sebastianlehner improvedmachinelearningalgorithmforpredictinggroundstateproperties AT richardkueng improvedmachinelearningalgorithmforpredictinggroundstateproperties AT johnpreskill improvedmachinelearningalgorithmforpredictinggroundstateproperties |