Neural network enhanced hybrid quantum many-body dynamical distributions
dc.contributor | Aalto-yliopisto | fi |
dc.contributor | Aalto University | en |
dc.contributor.author | Koch, Rouven | |
dc.contributor.author | Lado, Jose | |
dc.contributor.department | Department of Applied Physics | |
dc.contributor.department | Correlated Quantum Materials (CQM) | |
dc.date.accessioned | 2021-08-04T06:47:49Z | |
dc.date.available | 2021-08-04T06:47:49Z | |
dc.date.issued | 2021-07-29 | |
dc.description.abstract | Computing dynamical distributions in quantum many-body systems represents one of the paradigmatic open problems in theoretical condensed matter physics. Despite the existence of different techniques both in real-time and frequency space, computational limitations often dramatically constrain the physical regimes in which quantum many-body dynamics can be efficiently solved. Here we show that the combination of machine-learning methods and complementary many-body tensor network techniques substantially decreases the computational cost of quantum many-body dynamics. We demonstrate that combining kernel polynomial techniques and real-time evolution, together with deep neural networks, allows to compute dynamical quantities faithfully. Focusing on many-body dynamical distributions, we show that this hybrid neural-network many-body algorithm, trained with single-particle data only, can efficiently extrapolate dynamics for many-body systems without prior knowledge. Importantly, this algorithm is shown to be substantially resilient to numerical noise, a feature of major importance when using this algorithm together with noisy many-body methods. Ultimately, our results provide a starting point towards neural-network powered algorithms to support a variety of quantum many-body dynamical methods, that could potentially solve computationally expensive many-body systems in a more efficient manner. | en |
dc.description.version | Peer reviewed | en |
dc.format.extent | 10 | |
dc.format.mimetype | application/pdf | |
dc.identifier.citation | Koch , R & Lado , J 2021 , ' Neural network enhanced hybrid quantum many-body dynamical distributions ' , PHYSICAL REVIEW RESEARCH , vol. 3 , no. 3 , 033102 . https://doi.org/10.1103/PhysRevResearch.3.033102 | en |
dc.identifier.doi | 10.1103/PhysRevResearch.3.033102 | |
dc.identifier.issn | 2643-1564 | |
dc.identifier.other | PURE UUID: ff5fec24-9a0d-4f03-8a77-69233a9d51e6 | |
dc.identifier.other | PURE ITEMURL: https://research.aalto.fi/en/publications/ff5fec24-9a0d-4f03-8a77-69233a9d51e6 | |
dc.identifier.other | PURE LINK: http://www.scopus.com/inward/record.url?scp=85115887651&partnerID=8YFLogxK | |
dc.identifier.other | PURE FILEURL: https://research.aalto.fi/files/66220713/Neural_network_enhanced_hybrid_quantum_many_body_dynamical_distributions.PhysRevResearch.3.033102.pdf | |
dc.identifier.uri | https://aaltodoc.aalto.fi/handle/123456789/109011 | |
dc.identifier.urn | URN:NBN:fi:aalto-202108048255 | |
dc.language.iso | en | en |
dc.publisher | American Physical Society | |
dc.relation.ispartofseries | PHYSICAL REVIEW RESEARCH | en |
dc.relation.ispartofseries | Volume 3, issue 3 | en |
dc.rights | openAccess | en |
dc.title | Neural network enhanced hybrid quantum many-body dynamical distributions | en |
dc.type | A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä | fi |
dc.type.version | publishedVersion |