Tensor Network Computations That Capture Strict Variationality, Volume Law Behavior, and the Efficient Representation of Neural Network States
We introduce a change of perspective on tensor network states that is defined by the
computational graph of the contraction of an amplitude. The resulting class of states, which …
computational graph of the contraction of an amplitude. The resulting class of states, which …
Tensor networks enable the calculation of turbulence probability distributions
Predicting the dynamics of turbulent fluid flows has long been a central goal of science and
engineering. Yet, even with modern computing technology, accurate simulation of all but the …
engineering. Yet, even with modern computing technology, accurate simulation of all but the …
Chebyshev approximation and composition of functions in matrix product states for quantum-inspired numerical analysis
JJ Rodríguez-Aldavero, P García-Molina… - arXiv preprint arXiv …, 2024 - arxiv.org
This work explores the representation of univariate and multivariate functions as matrix
product states (MPS), also known as quantized tensor-trains (QTT). It proposes an algorithm …
product states (MPS), also known as quantized tensor-trains (QTT). It proposes an algorithm …
[PDF][PDF] Diagrammatic Monte Carlo: recent developments and applications to the Hubbard model
M Ferrero - 2023 - hal.science
Materials with strong electronic correlations host among the most fascinating phenomena of
modern condensed matter physics. They originate from a complex interplay between the …
modern condensed matter physics. They originate from a complex interplay between the …