Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Giuseppe Scriva"'
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 3, p 035015 (2024)
In recent years, machine learning models, chiefly deep neural networks, have revealed suited to learn accurate energy-density functionals from data. However, problematic instabilities have been shown to occur in the search of ground-state density pro
Externí odkaz:
https://doaj.org/article/d33cfce152d14569b32f085764b6bdc5
Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning
Publikováno v:
SciPost Physics, Vol 15, Iss 1, p 018 (2023)
Adiabatic quantum computers, such as the quantum annealers commercialized by D-Wave Systems Inc., are routinely used to tackle combinatorial optimization problems. In this article, we show how to exploit them to accelerate equilibrium Markov chain Mo
Externí odkaz:
https://doaj.org/article/56529e877f6749e09321ed8194066814
Publikováno v:
Physical review. E. 106(4-2)
Machine-learned regression models represent a promising tool to implement accurate and computationally affordable energy-density functionals to solve quantum many-body problems via density functional theory. However, while they can easily be trained