Quantum System Noise Matrix Adjustment: A Dynamic Adaptive Algorithm

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum systems exhibit inherent sensitivity to environmental noise, posing significant challenges to precise control and optimization. Conventional noise matrix adjustment techniques often lack the adaptability needed to effectively mitigate these fluctuations. This paper introduces a novel adaptive algorithm, "Quantum Noise Matrix Adjustment" (QNMA), designed to dynamically adjust quantum system noise matrices based on real-time observations. The algorithm leverages a self-correcting mechanism that continually refines the noise matrix to maintain system stability and accuracy, offering a significant advancement in control capabilities. We present a detailed analysis of the QNMA's design and performance, demonstrating its effectiveness through simulations and preliminary experimental validation. The core mechanism focuses on continuous noise adaptation using a modified quantum state evolution, leading to a robust and tunable control strategy.

DOI: 10.5281/zenodo.22268504

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