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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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Data supporting the publication "Many-body quantum sign structures as non-glassy Ising models"

Authors: Westerhout, Tom; Katsnelson, Mikhail I.; Bagrov, Andrey A.;

Data supporting the publication "Many-body quantum sign structures as non-glassy Ising models"

Abstract

This repository contains all raw data that were used to draw conclusions and generate figures for the paper: "Many-body quantum sign structures as non-glassy Ising models" by Westerhout, T., Katsnelson, M. I., & Bagrov, A. A. Abstract: The non-trivial phase structure of the eigenstates of many-body quantum systems severely limits the applicability of quantum Monte Carlo, variational, and machine learning methods. Here, we study real-valued signful ground-state wave functions of frustrated quantum spin systems and, assuming that the tasks of finding wave function amplitudes and signs can be separated, show that the signs can be easily bootstrapped from the amplitudes. We map the problem of finding the sign structure to an auxiliary classical Ising model defined on a subset of the Hilbert space basis. We show that the Ising model does not exhibit significant frustrations even for highly frustrated parental quantum systems, and is solvable with a fully deterministic O(K log K)-time combinatorial algorithm (where K is the Ising model size). Given the ground state amplitudes, we reconstruct the signs of the ground states of several frustrated quantum models, thereby revealing the hidden simplicity of many-body sign structures.

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Keywords

exact diagonalization, spin systems, variational Monte Carlo, quantum many body, sign problem, neural networks, ising model

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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