Quantum Suppression of Decoherence in Adaptive Quantum Algorithms for Optimization

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel approach to optimizing quantum algorithms for complex problems, specifically focusing on mitigating the detrimental effects of decoherence. The core idea centers around an adaptive strategy where the algorithm's parameters and execution are dynamically adjusted based on real-time monitoring of the algorithm's decoherence level. We propose a quantum decoherence monitoring system employing quantum sensors to measure coherence times of qubits. These measurements then inform adjustments to the algorithm's parameters, such as the angles and durations of quantum gates. The system aims to maximize computational efficiency by minimizing the impact of decoherence. This approach represents a significant departure from traditional quantum algorithm design, which typically lacks this dynamic suppression mechanism. The theoretical framework and the proposed methodology are presented, outlining a promising pathway for enhancing the performance of quantum algorithms across various optimization tasks. The key metric of success is a quantifiable increase in the accuracy and speed of the algorithm, directly correlated to the effectiveness of the decoherence suppression strategy. The presented system provides a framework for continuous optimization, potentially leading to algorithms capable of tackling increasingly complex problems.

DOI: 10.5281/zenodo.22294021

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