Zobrazeno 1 - 10
of 409
pro vyhledávání: '"Pilati, S."'
Autor:
Brodoloni, L., Pilati, S.
A continuous-time projection quantum Monte Carlo algorithm is employed to simulate the ground state of a short-range quantum spin-glass model, namely, the two-dimensional Edwards-Anderson Hamiltonian with transverse field, featuring Gaussian nearest-
Externí odkaz:
http://arxiv.org/abs/2407.05978
Publikováno v:
Molecules 2023, 28, 1661
In recent years, machine learning has been proposed as a promising strategy to build accurate scoring functions for computational docking finalized to numerically empowered drug discovery. However, the latest studies have suggested that over-optimist
Externí odkaz:
http://arxiv.org/abs/2212.03202
Autor:
Spada, G., Parisi, L., Pascual, G., Parker, N. G., Billam, T. P., Pilati, S., Boronat, J., Giorgini, S.
Publikováno v:
SciPost Phys. 15, 171 (2023)
We investigate the magnetic behavior of finite-temperature repulsive two-component Bose mixtures by means of exact path-integral Monte-Carlo simulations. Novel algorithms are implemented for the free energy and the chemical potential of the two compo
Externí odkaz:
http://arxiv.org/abs/2211.09574
Publikováno v:
Quantum Sci. Technol. 8, 025022 (2023)
Predicting the output of quantum circuits is a hard computational task that plays a pivotal role in the development of universal quantum computers. Here we investigate the supervised learning of output expectation values of random quantum circuits. D
Externí odkaz:
http://arxiv.org/abs/2206.10348
Publikováno v:
Condens. Matter 2022, 7, 30
We provide a detailed description of the path-integral Monte Carlo worm algorithm used to exactly calculate the thermodynamics of Bose systems in the canonical ensemble. The algorithm is fully consistent with periodic boundary conditions, that are ap
Externí odkaz:
http://arxiv.org/abs/2203.00010
Publikováno v:
Phys. Rev. A 103, 063314 (2021)
The ground-state properties of two-component repulsive Fermi gases in two dimensions are investigated by means of fixed-node diffusion Monte Carlo simulations. The energy per particle is determined as a function of the intercomponent interaction stre
Externí odkaz:
http://arxiv.org/abs/2103.13251
Publikováno v:
Phys. Rev. E 102, 033301 (2020)
Supervised machine learning is emerging as a powerful computational tool to predict the properties of complex quantum systems at a limited computational cost. In this article, we quantify how accurately deep neural networks can learn the properties o
Externí odkaz:
http://arxiv.org/abs/2005.14290
Autor:
Pilati, S., Pieri, P.
Publikováno v:
Phys. Rev. E 101, 063308 (2020)
In recent years, generative artificial neural networks based on restricted Boltzmann machines (RBMs) have been successfully employed as accurate and flexible variational wave functions for clean quantum many-body systems. In this article we explore t
Externí odkaz:
http://arxiv.org/abs/2003.09765
Publikováno v:
Phys. Rev. E 101, 053312 (2020)
The autoregressive neural networks are emerging as a powerful computational tool to solve relevant problems in classical and quantum mechanics. One of their appealing functionalities is that, after they have learned a probability distribution from a
Externí odkaz:
http://arxiv.org/abs/2002.04292
Publikováno v:
Phys. Rev. B 100, 214303 (2019)
Quantum tunneling is a valuable resource exploited by quantum annealers to solve complex optimization problems. Tunneling events also occur during projective quantum Monte Carlo (PQMC) simulations, and in a class of problems characterized by a double
Externí odkaz:
http://arxiv.org/abs/1908.10151