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3425. Efficient Prediction of Quantum Ground State Properties via Classical Machine Learning with Geometric Locality
Invited abstract in session MD-42: Optimization in Quantum Information, stream Quantum Computing Optimization.
Monday, 14:30-16:00Room: 98 (building: 306)
Authors (first author is the speaker)
1. | Viet Tran
|
Institute for Integrated Circuits, Johannes Kepler University |
Abstract
An fundamental optimization problem in quantum physics is finding the ground state of quantum many-body systems.
Addressing this challenge, our presentation highlights numerical experiments that utilize a classical machine learning (ML) algorithm leveraging a geometric locality inductive bias to efficiently predict ground state properties. Our experiments on systems up to 45 qubits illustrate the model's ability to accurately predict ground state properties with only O(log(n)) instances from similar Hamiltonians, demonstrating a substantial efficiency improvement over previous state of the art methods with polynomial sample complexity. These findings validate theoretical improvements on the sample complexity bound for geometrically informed ML algorithms.
Additionally, if time allows, we will delve into preliminary findings from ongoing research in the domain of shadow tomography facilitated by diffusion models, exploring their potential to further advance our understanding of quantum systems.
Keywords
- Machine Learning
Status: accepted
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