基于自适应的物理学模拟的量子环境优化

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum environment optimization is a crucial challenge in quantum computing, directly impacting the performance of quantum algorithms and the efficiency of quantum information storage. Traditional quantum simulation methods often rely on fixed parameters, limiting the potential for significant optimization. This paper presents a novel approach leveraging adaptive physics simulation, dynamically adjusting the quantum environment during the simulation process to enhance system performance. We introduce a self-optimizing algorithm that continuously evaluates and modifies the quantum system's environment based on gradients, resulting in improved accuracy and reduced computational cost. The core mechanism focuses on creating a feedback loop where the simulation parameters are continually refined based on the outcome of the simulation, leading to a more efficient and robust quantum simulation process. We demonstrate the effectiveness of this approach through a series of simulations focusing on variational quantum eigensolver (VQE) and quantum phase estimation (QPE) tasks, achieving substantial improvements in both accuracy and computational time compared to traditional methods. The results highlight the potential of this adaptive approach to address key bottlenecks in quantum simulation.

DOI: 10.5281/zenodo.22220286

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