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SUMMARY:JCMS: Simulating strongly correlated systems with neural quantum s
 tates
DTSTART:20260427T083000Z
DTEND:20260427T103000Z
DTSTAMP:20260517T004700Z
UID:indico-event-9670@indico.fysik.su.se
DESCRIPTION:Speakers: Hannah Lange (Ludwig Maximilians University Munich)\
 n\nSimulating strongly correlated many-body quantum systems remains a majo
 r challenge for existing numerical and experimental platforms\, yet it is 
 essential for understanding a wide range of materials and phenomena\, incl
 uding high-temperature superconductivity. In this talk\, I will present a 
 machine-learning framework for large-scale simulations of many-body quantu
 m systems. This approach uses neural networks to parametrize quantum many-
 body wave function coefficients\, an idea known as neural quantum states (
 NQS). I will outline recent advances in NQS and demonstrate how they provi
 de access to regimes beyond the reach of conventional methods such as matr
 ix product states. As a concrete example\, I will discuss our studies of s
 ingle-layer and bilayer models relevant to cuprate and bilayer nickelate s
 uperconductors using fermionic versions of NQS. Finally\, I will present h
 ow NQS can be used to simulate multiband models relevant to a range of unc
 onventional superconductors.\n\nhttps://indico.fysik.su.se/event/9670/
LOCATION:Albano 3: 6228 - Mega (22 seats) (Albano Building 3)
URL:https://indico.fysik.su.se/event/9670/
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