Probabilistic Simulation of Quantum Systems using Monte Carlo Methods with Adaptive Sampling

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper investigates the efficient probabilistic simulation of quantum systems using Monte Carlo methods, addressing the computational challenges inherent in these simulations. The core idea is to implement an adaptive sampling strategy, leveraging a Bayesian framework to dynamically adjust the sampling rate based on the estimated variance of the simulation results. This approach minimizes unnecessary computations by focusing sampling efforts on regions of higher uncertainty, thereby enhancing the overall efficiency of the simulation process. The methodology presented significantly improves upon traditional Monte Carlo techniques, offering a promising avenue for tackling increasingly complex quantum systems. The paper details the mathematical formulation of the adaptive sampling process and demonstrates its effectiveness through a conceptual analysis.

DOI: 10.5281/zenodo.22222288

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