Adaptive Quantum Computing Quantum Simulation Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates the application of a self-adaptive quantum simulation optimization algorithm to enhance the accuracy and efficiency of quantum simulations. Traditional simulation methods often rely on fixed parameters, limiting the potential for optimization. This research proposes a novel algorithm that dynamically adjusts simulation parameters based on simulation results, resulting in continuous refinement and improved performance. The core mechanism focuses on a reinforcement learning-inspired approach to optimize the simulation process. This adaptive strategy offers a significant advancement in quantum simulation, addressing limitations of static optimization techniques and promising improved results across a range of quantum systems. The paper details the algorithm's design, implementation, and results, demonstrating its potential for enhancing the capabilities of quantum simulations.

DOI: 10.5281/zenodo.22200191

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