Projekteja vuodessa
Abstrakti
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.
Alkuperäiskieli | Englanti |
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Artikkeli | 033102 |
Sivumäärä | 10 |
Julkaisu | PHYSICAL REVIEW RESEARCH |
Vuosikerta | 3 |
Numero | 3 |
DOI - pysyväislinkit | |
Tila | Julkaistu - 29 heinäk. 2021 |
OKM-julkaisutyyppi | A1 Julkaistu artikkeli, soviteltu |
Sormenjälki
Sukella tutkimusaiheisiin 'Neural network enhanced hybrid quantum many-body dynamical distributions'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.Projektit
- 2 Aktiivinen
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Tekninen murto-osa-kvantiaine kierretyissä van der Waals -materiaaleissa
01/09/2020 → 31/08/2025
Projekti: Academy of Finland: Other research funding
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Tekninen murto-osa-kvantiaine kierretyissä van der Waals -materiaaleissa
Lado, J., Hyart, T., Koch, R. & Kumar, P.
01/09/2020 → 31/08/2023
Projekti: Academy of Finland: Other research funding