Title: Adaptive Quantum Monte Carlo for Material Design

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum optimization, particularly utilizing Monte Carlo methods, offers a promising pathway to accelerate the design of novel materials. However, classical Monte Carlo simulations often suffer from slow convergence and high computational cost. This paper proposes an adaptive quantum Monte Carlo algorithm that dynamically adjusts simulation parameters during the design process, aiming to significantly enhance the efficiency of material design. We introduce a novel Bayesian optimization framework incorporating gradient descent to optimize simulation parameters, leading to improved convergence rates and reduced computational time. The algorithm's adaptability is achieved through a feedback loop that continuously evaluates the quality of the simulation and adjusts the parameters accordingly. This results in a more efficient and robust approach to material exploration and design. The study demonstrates the effectiveness of this adaptive quantum Monte Carlo method in generating promising material candidates, highlighting its potential to revolutionize the field of materials science.

DOI: 10.5281/zenodo.22242720

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