Improved machine learning algorithm for predicting ground state properties

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 predi...

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Bibliographic Details
Published in:Nature communications Vol. 15; no. 1; pp. 895 - 8
Main Authors: Lewis, Laura, Huang, Hsin-Yuan, Tran, Viet T., Lehner, Sebastian, Kueng, Richard, Preskill, John
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 30.01.2024
Nature Publishing Group
Nature Portfolio
Subjects:
639/705/117
639/766/483/1139
639/766/483/481
Algorithms
CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS
Computer science
Ground state
Humanities and Social Sciences
Learning algorithms
Machine learning
MATHEMATICS AND COMPUTING
multidisciplinary
Quantum information
Quantum mechanics
Quantum theory
Qubits (quantum computing)
Science
Science & Technology
Science (multidisciplinary)
ISSN:2041-1723, 2041-1723
Online Access:Get full text
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Summary: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 O ( log ( n ) ) data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require O ( n c ) data for a large constant c . Furthermore, the training and prediction time of the proposed ML model scale as 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. Recent work proposed a machine learning algorithm for predicting ground state properties of quantum many-body systems that outperforms any non-learning classical algorithm but requires extensive training data. Lewis et al. present an improved algorithm with exponentially reduced training data requirements.
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USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
SC0020290; NA0003525; PHY-1733907
National Science Foundation (NSF)
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-024-45014-7