Overcoming the numerical sign problem in the Wigner dynamics via adaptive particle annihilation
Yunfeng Xiong, Sihong Shao

TL;DR
This paper introduces SPADE, an adaptive particle annihilation algorithm that effectively mitigates the numerical sign problem in high-dimensional Wigner simulations by clustering particles based on discrepancy control, enabling efficient quantum dynamics modeling.
Contribution
The paper presents SPADE, a novel adaptive clustering method for particle annihilation that overcomes the curse of dimensionality in Wigner simulations, improving efficiency and accuracy.
Findings
SPADE effectively reduces the sign problem in 6-D and 12-D phase space.
Benchmark results show SPADE's accuracy comparable to grid-based deterministic solvers.
SPADE enables simulation of complex quantum systems with high-dimensional phase space.
Abstract
The infamous numerical sign problem poses a fundamental obstacle to particle-based stochastic Wigner simulations in high dimensional phase space. Although the existing particle annihilation via uniform mesh significantly alleviates the sign problem when dimensionality D 4, the mesh size grows dramatically when D 6 due to the curse of dimensionality and consequently makes the annihilation very inefficient. In this paper, we propose an adaptive particle annihilation algorithm, termed Sequential-clustering Particle Annihilation via Discrepancy Estimation (SPADE), to overcome the sign problem. SPADE follows a divide-and-conquer strategy: Adaptive clustering of particles via controlling their number-theoretic discrepancies and independent random matching in each group, and it may learn the minimal amount of particles that can accurately capture the non-classicality of the Wigner…
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Taxonomy
TopicsStatistical Methods and Bayesian Inference · Bayesian Methods and Mixture Models
