Quantum State Dynamics: A Novel Adaptive Optimization Approach

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum state dynamics represent a significant advancement in quantum computation, offering the potential for enhanced qubit coherence and increased computational power. However, the inherent complexity of quantum systems necessitates sophisticated optimization techniques to achieve optimal performance. This paper introduces a novel algorithm, Adaptive Optimization Based on Dynamic Quantum State Distributions, designed to dynamically adjust the probability distribution of quantum states based on system feedback. The core mechanism leverages a feedback loop to continuously refine the state distribution, improving both performance and stability. This approach contrasts with traditional optimization methods which operate on static probability distributions, promising a more robust and adaptable framework for quantum system design. This work explores the mathematical foundations and practical implementation of this algorithm, demonstrating its effectiveness through simulations and preliminary experiments. The focus is on enhancing quantum state coherence and exploring the potential for improved computational outcomes.

DOI: 10.5281/zenodo.22625413

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