基于自适应的量子态的概率分布优化

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum state optimization is a crucial challenge in quantum computing, aiming to enhance the performance of quantum systems. Traditional optimization methods often struggle with the inherent complexity of quantum systems, requiring extensive parameter tuning. This paper introduces a novel approach, termed "Adaptive Quantum State Optimization" (AQSO), which dynamically adjusts quantum state parameters through an adaptive algorithm to optimize quantum system performance. We demonstrate the effectiveness of AQSO through a series of simulations and analyses, showcasing improved coherence times, gate fidelity, and overall system performance compared to existing optimization techniques. The core mechanism focuses on systematically adjusting key parameters within a quantum state space, leveraging self-adaptive refinement to achieve optimal quantum states.

DOI: 10.5281/zenodo.22184599

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