Accurate Learning of Equivariant Quantum Systems from a Single Ground State
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Abstract (arXiv)
Predicting properties across system parameters is an important task in quantum physics, with applications ranging from molecular dynamics to variational quantum algorithms. Recently, provably efficient algorithms to solve this task for ground states within a gapped phase were developed. Here we dramatically improve the efficiency of these algorithms by showing how to learn properties of all ground states for systems with periodic boundary conditions from a single ground state sample. We prove that the prediction error tends to zero in the thermodynamic limit and numerically verify the results.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 48 references that are in the map. First posted on arXiv 20 May 2024.
Builds on
10 of 48 references.
References · 10
- 2017Neural Message Passing for Quantum Chemistrycited by 3,487
- 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 708
- 2020Predicting Many Properties of a Quantum System from Very Few Measurementscited by 233
- 2019Ab-Initio Solution of the Many-Electron Schr"odinger Equation with Deep Neural Networkscited by 200
- 2022Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulationscited by 95
- 2021Provably efficient machine learning for quantum many-body problemscited by 91
- 2019Machine Learning from a Continuous Viewpointcited by 50
- 2023A survey on the complexity of learning quantum statescited by 32
- 2023Improved machine learning algorithm for predicting ground state propertiescited by 22
- 2023Efficient Learning of Long-Range and Equivariant Quantum Systemscited by 3
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