Zobrazeno 1 - 10
of 369
pro vyhledávání: '"Shinaoka A"'
We propose a physics-informed neural network (PINN) model to efficiently predict the self-energy of Anderson impurity models (AIMs) based on the Lehmann representation. As an example, we apply the PINN model to a single-orbital AIM (SAIM) for a nonin
Externí odkaz:
http://arxiv.org/abs/2411.18835
Autor:
Rohshap, Stefan, Ritter, Marc K., Shinaoka, Hiroshi, von Delft, Jan, Wallerberger, Markus, Kauch, Anna
We present the first application of quantics tensor trains (QTTs) and tensor cross interpolation (TCI) to the solution of a full set of self-consistent equations for multivariate functions, the so-called parquet equations. We show that the steps need
Externí odkaz:
http://arxiv.org/abs/2410.22975
LiV$_2$O$_4$ is a member of the so-called $3d$ heavy fermion compounds, with effective electron mass exceeding 60 times the free electron mass, comparable to $4f$ heavy fermion compounds. The origin of the strong electron correlation in combination w
Externí odkaz:
http://arxiv.org/abs/2410.08515
Analytic continuation (AC) from imaginary-time Green's function to spectral function is essential in the numerical analysis of dynamical properties in quantum many-body systems. However, this process faces a fundamental challenge: it is an ill-posed
Externí odkaz:
http://arxiv.org/abs/2409.01509
Autor:
Fernández, Yuriel Núñez, Ritter, Marc K., Jeannin, Matthieu, Li, Jheng-Wei, Kloss, Thomas, Louvet, Thibaud, Terasaki, Satoshi, Parcollet, Olivier, von Delft, Jan, Shinaoka, Hiroshi, Waintal, Xavier
The tensor cross interpolation (TCI) algorithm is a rank-revealing algorithm for decomposing low-rank, high-dimensional tensors into tensor trains/matrix product states (MPS). TCI learns a compact MPS representation of the entire object from a tiny t
Externí odkaz:
http://arxiv.org/abs/2407.02454
Tensor cross interpolation (TCI) is a powerful technique for learning a tensor train (TT) by adaptively sampling a target tensor based on an interpolation formula. However, when the tensor evaluations contain random noise, optimizing the TT is more a
Externí odkaz:
http://arxiv.org/abs/2405.12730
Feynman diagrams are an essential tool for simulating strongly correlated electron systems. However, stochastic quantum Monte Carlo (QMC) sampling suffers from the sign problem, e.g., when solving a multiorbital quantum impurity model. Recently, two
Externí odkaz:
http://arxiv.org/abs/2405.06440
Space-time dependence of imaginary-time propagators, vital for \textit{ab initio} and many-body calculations based on quantum field theories, has been revealed to be compressible using Quantum Tensor Trains (QTTs) [Phys. Rev. X {\bf 13}, 021015 (2023
Externí odkaz:
http://arxiv.org/abs/2403.09161
Predicting the properties of strongly correlated materials is a significant challenge in condensed matter theory. The widely used dynamical mean-field theory faces difficulty in solving quantum impurity models numerically. Hybrid quantum--classical a
Externí odkaz:
http://arxiv.org/abs/2312.04105
Publikováno v:
Phys. Rev. B 109, 165135 (2024)
The nonequilibrium Green's function formalism provides a versatile and powerful framework for numerical studies of nonequilibrium phenomena in correlated many-body systems. For calculations starting from an equilibrium initial state, a standard appro
Externí odkaz:
http://arxiv.org/abs/2312.03809