Beyond Schwinger boson mean-field theory: Numerical simulations of quantum and classical Heisenberg models
ORAL
Abstract
Schwinger boson “parton” constructions provide a systematic way to study frustrated quantum spin lattice Hamiltonians via bosonic formal methods without making limiting assumptions about the symmetries of the ground state. Despite this versatility, thermodynamic treatments are limited to mean-field and “large N” SU(N) approaches because of highly oscillatory statistical weights that render Monte Carlo methods inapplicable. Furthermore, Schwinger boson methods require careful treatment of constraints that faithfully map the bosonic Fock space to a spin-S subspace.
Here, we present progress in numerically sampling the Schwinger boson coherent-state path-integral representation of quantum Heisenberg models at finite temperature. We introduce a projected complex Langevin numerical technique that enforces the Schwinger boson constraints exactly at each sampling iteration, leading to improvements in the approach’s stability and efficiency. We apply our method to a S = 3/2 frustrated triangular antiferromagnet to demonstrate the technique’s access to equilibrium spin textures, spin-spin correlations, properties, and comparisons of quantum and classical effects. We conclude with a discussion of the current method’s limitations and potential applications.
Here, we present progress in numerically sampling the Schwinger boson coherent-state path-integral representation of quantum Heisenberg models at finite temperature. We introduce a projected complex Langevin numerical technique that enforces the Schwinger boson constraints exactly at each sampling iteration, leading to improvements in the approach’s stability and efficiency. We apply our method to a S = 3/2 frustrated triangular antiferromagnet to demonstrate the technique’s access to equilibrium spin textures, spin-spin correlations, properties, and comparisons of quantum and classical effects. We conclude with a discussion of the current method’s limitations and potential applications.
*We acknowledge support from the National Science Foundation under Grant No DMR-2104255. Use was made of computational facilities purchased with funds from the National Science Foundation (CNS-1725797) and administered by the Center for Scientific Computing (CSC). This work made use of the BioPACIFIC Materials Innovation Platform computing resources of the National Science Foundation Award No. DMR-1933487. The CSC is supported by the California NanoSystems Institute and the Materials Research Science and Engineering Center (MRSEC; NSF DMR 2308708) at UC Santa Barbara.
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Presenters
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Ethan C McGarrigle
- University of California, Santa Barbara