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SUMMARY:Towards the Optimizer that NQS Deserves
DTSTART;VALUE=DATE-TIME:20251119T163000Z
DTEND;VALUE=DATE-TIME:20251119T164500Z
DTSTAMP;VALUE=DATE-TIME:20260421T163311Z
UID:indico-contribution-28994@indico.ific.uv.es
DESCRIPTION:Speakers: Javier Rozalén Sarmiento (Universitat de Barcelona)
 \nIn this talk\, I will be covering one of the newest methods for nuclear 
 structure calculations\, Neural Quantum States (NQS). While it is not spec
 ific to nuclear physics [1\,2]\, since its first application for computing
  the deuteron bound state [3]\, its application to nuclear ground states h
 as been consistently gaining momentum [4\,5]. The claim of NQS is that\, b
 y introducing a highly-expressive neural-network ansatz in a Variational M
 onte Carlo (VMC) setting\, we can obtain a system’s wave function with o
 nly a polynomial cost in the number of particles. In the talk\, I will bri
 efly cover the optimization algorithms that power NQS nowadays\, to then p
 resent our most novel optimizer\, Decisional Gradient Descent (DGD) [6]. W
 hereas Stochastic Reconfiguration (SR) has been the preferred optimizer in
  VMC calculations\, we have shown that it is not well-suited as a second-o
 rder optimization algorithm. Whereas SR performs poorly when used within N
 ewton’s method\, DGD manages to reach the ground state of a variety of p
 hysical systems in a reduced number of iterations. Having been put to test
  in both continuous-coordinate and discrete-coordinate systems\, this work
  paves the way for subsequent applications to the more complex nuclear sys
 tems.\n\n[1] G. Carleo and M. Troyer\, Science 355 602-606 (2017)\n[2] D. 
 Pfau\, J. Spencer et al.\, Phys. Rev. Research 2\, 033429 (2020)\n[3] J. K
 eeble and A. Rios\, Phys. Lett. B 135743 (2020)\n[4] A. Gnech\, B. Fore et
  al.\, Phys. Rev. Lett. 133\, 142501 (2024)\n[5] M. Rigo\, B. Hall et al.\
 , Phys. Rev. E 107\, 025310 (2023)\n[6] M. Drissi\, J. Keeble et al.\, Phi
 l. Trans. R. Soc. A 38220240057 (2024)\n\nhttps://indico.ific.uv.es/event/
 8035/contributions/28994/
LOCATION:
URL:https://indico.ific.uv.es/event/8035/contributions/28994/
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