Dynamic Adaptive Quantum State Optimization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel dynamic self-adaptive quantum state optimization technique. Traditional quantum state optimization methods rely on static parameter settings, which are often inadequate for dynamic environments. Our approach leverages a dynamic adjustment of quantum state parameters, enabling precise control and optimization of quantum states. We propose a method that continuously refines the quantum state based on input data, achieving a significant improvement in state fidelity and control compared to static optimization techniques. The core mechanism revolves around a feedback loop that adjusts parameters to minimize errors and maximize desired outcomes. The resulting system demonstrates superior performance in various quantum control applications.

DOI: 10.5281/zenodo.22263573

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