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Egile Nagusiak: Chen, Yu-Qin, Zhang, Shi-Xin
Formatua: Preprint
Argitaratua: 2024
Gaiak:
Sarrera elektronikoa:https://arxiv.org/abs/2411.18921
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author Chen, Yu-Qin
Zhang, Shi-Xin
author_facet Chen, Yu-Qin
Zhang, Shi-Xin
contents In the pursuit of numerically identifying the ground state of quantum many-body systems, approximate quantum wavefunction ansatzes are commonly employed. This study focuses on the spectral decomposition of these approximate quantum many-body states into exact eigenstates of the target Hamiltonian. The energy spectral decomposition could reflect the intricate physics at the interplay between quantum systems and numerical algorithms. Here we examine various parameterized wavefunction ansatzes constructed from neural networks, tensor networks, and quantum circuits, employing differentiable programming to numerically approximate ground states and imaginary-time evolved states. Our findings reveal a consistent exponential decay pattern in the spectral contributions of approximate quantum states across different ansatzes, optimization objectives, and quantum systems, characterized by small decay rates denoted as inverse effective temperatures. The effective temperature is related to ansatz expressiveness and accuracy and shows phase transition behaviors in learning imaginary-time evolved states. The universal picture and unique features suggest the significance and potential of the effective temperature in characterizing approximate quantum states.
format Preprint
id arxiv_https___arxiv_org_abs_2411_18921
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Effective temperature in approximate quantum many-body states
Chen, Yu-Qin
Zhang, Shi-Xin
Quantum Physics
Strongly Correlated Electrons
In the pursuit of numerically identifying the ground state of quantum many-body systems, approximate quantum wavefunction ansatzes are commonly employed. This study focuses on the spectral decomposition of these approximate quantum many-body states into exact eigenstates of the target Hamiltonian. The energy spectral decomposition could reflect the intricate physics at the interplay between quantum systems and numerical algorithms. Here we examine various parameterized wavefunction ansatzes constructed from neural networks, tensor networks, and quantum circuits, employing differentiable programming to numerically approximate ground states and imaginary-time evolved states. Our findings reveal a consistent exponential decay pattern in the spectral contributions of approximate quantum states across different ansatzes, optimization objectives, and quantum systems, characterized by small decay rates denoted as inverse effective temperatures. The effective temperature is related to ansatz expressiveness and accuracy and shows phase transition behaviors in learning imaginary-time evolved states. The universal picture and unique features suggest the significance and potential of the effective temperature in characterizing approximate quantum states.
title Effective temperature in approximate quantum many-body states
topic Quantum Physics
Strongly Correlated Electrons
url https://arxiv.org/abs/2411.18921