Neural network enhanced hybrid quantum many-body dynamical distributions

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorKoch, Rouven
dc.contributor.authorLado, Jose
dc.contributor.departmentDepartment of Applied Physics
dc.contributor.departmentCorrelated Quantum Materials (CQM)
dc.date.accessioned2021-08-04T06:47:49Z
dc.date.available2021-08-04T06:47:49Z
dc.date.issued2021-07-29
dc.description.abstractComputing 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.versionPeer revieweden
dc.format.extent10
dc.format.mimetypeapplication/pdf
dc.identifier.citationKoch , 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.3.033102en
dc.identifier.doi10.1103/PhysRevResearch.3.033102
dc.identifier.issn2643-1564
dc.identifier.otherPURE UUID: ff5fec24-9a0d-4f03-8a77-69233a9d51e6
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/ff5fec24-9a0d-4f03-8a77-69233a9d51e6
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85115887651&partnerID=8YFLogxK
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/66220713/Neural_network_enhanced_hybrid_quantum_many_body_dynamical_distributions.PhysRevResearch.3.033102.pdf
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/109011
dc.identifier.urnURN:NBN:fi:aalto-202108048255
dc.language.isoenen
dc.publisherAmerican Physical Society
dc.relation.ispartofseriesPHYSICAL REVIEW RESEARCHen
dc.relation.ispartofseriesVolume 3, issue 3en
dc.rightsopenAccessen
dc.titleNeural network enhanced hybrid quantum many-body dynamical distributionsen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion

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