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May 1, 2024Entropy17 citationsOpen Access

Monte Carlo Based Techniques for Quantum Magnets with Long-Range Interactions

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PAPatrick AdelhardtFriedrich-Alexander-Universität Erlangen-NürnbergJKJan Alexander KoziolFriedrich-Alexander-Universität Erlangen-NürnbergALAnja LangheldFriedrich-Alexander-Universität Erlangen-Nürnberg

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Abstract

Long-range interactions are relevant for a large variety of quantum systems in quantum optics and condensed matter physics. In particular, the control of quantum–optical platforms promises to gain deep insights into quantum-critical properties induced by the long-range nature of interactions. From a theoretical perspective, long-range interactions are notoriously complicated to treat. Here, we give an overview of recent advancements to investigate quantum magnets with long-range interactions focusing on two techniques based on Monte Carlo integration. First, the method of perturbative continuous unitary transformations where classical Monte Carlo integration is applied within the embedding scheme of white graphs. This linked-cluster expansion allows extracting high-order series expansions of energies and observables in the thermodynamic limit. Second, stochastic series expansion quantum Monte Carlo integration enables calculations on large finite systems. Finite-size scaling can then be used to determine the physical properties of the infinite system. In recent years, both techniques have been applied successfully to one- and two-dimensional quantum magnets involving long-range Ising, XY, and Heisenberg interactions on various bipartite and non-bipartite lattices. Here, we summarise the obtained quantum-critical properties including critical exponents for all these systems in a coherent way. Further, we review how long-range interactions are used to study quantum phase transitions above the upper critical dimension and the scaling techniques to extract these quantum critical properties from the numerical calculations.

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Cite This Study

Adelhardt et al. (2024) studied this question.

synapsesocial.com/papers/68e6c033b6db64358763f72bhttps://doi.org/10.3390/e26050401
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