Neural network enhanced hybrid quantum many-body dynamical distributions

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2021-07-29
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Language
en
Pages
10
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PHYSICAL REVIEW RESEARCH, Volume 3, issue 3
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.
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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