Quantum-Enhanced Iterative Algorithm for Monte Carlo Simulations

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel quantum-enhanced iterative algorithm for Monte Carlo simulations. We propose a hybrid approach combining the strengths of quantum annealing and QAOA to accelerate the exploration of the solution space and improve the accuracy of results. The algorithm systematically explores the solution space, leveraging quantum fluctuations to guide the search process and mitigate the effects of local optima. We demonstrate the effectiveness of this approach through simulations across a range of problems, highlighting its potential to significantly enhance the efficiency and reliability of Monte Carlo methods. The core mechanism centers around the quantum annealing process for initial exploration and the QAOA optimization loop for refinement. This research contributes to the development of more robust and faster simulations, offering a promising step towards broader applicability in various scientific and engineering disciplines.

DOI: 10.5281/zenodo.22200507

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