Scalable hybrid quantum Monte Carlo simulation of U(1) gauge field coupled to fermions on GPU
Kexin Feng, Chuang Chen, Zi Yang Meng

TL;DR
This paper introduces a GPU-accelerated hybrid quantum Monte Carlo algorithm for simulating U(1) gauge fields coupled to fermions, enabling large-scale studies of quantum spin liquids with improved efficiency and accuracy.
Contribution
The paper presents novel GPU-based computational techniques that significantly improve the scalability and efficiency of quantum Monte Carlo simulations for U(1) gauge theories coupled to fermions.
Findings
Achieved nearly linear scaling in computational complexity.
Simulated large system sizes up to 660×66×66.
Confirmed conformal nature of the U(1) Dirac spin liquid.
Abstract
We develop a GPU-accelerated hybrid quantum Monte Carlo (QMC) algorithm to solve the fundamental yet difficult problem of gauge field coupled to fermions, which gives rise to a Dirac spin liquid state under the description of (2+1)d quantum electrodynamics QED. The algorithm renders a good acceptance rate and, more importantly, nearly linear space-time volume scaling in computational complexity , where is the imaginary time dimension and is spatial volume, which is much more efficient than determinant QMC with scaling behavior of . Such acceleration is achieved via a collection of technical improvements, including (i) the design of the efficient problem-specific preconditioner, (ii) customized CUDA kernel for matrix-vector multiplication, and (iii) CUDA Graph implementation on the GPU. These advances allow us to simulate…
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