Quantum Adaptive Computing: An Adaptive Learning Algorithm

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum Adaptive Computing (QAC) represents a paradigm shift in computational paradigms, leveraging quantum mechanics to achieve enhanced processing capabilities. This paper investigates a novel adaptive learning algorithm, dubbed "Quantum Self-Adaptation Algorithm" (QSAA), designed to address the inherent limitations of traditional machine learning techniques. QSAA's core mechanism centers around the utilization of quantum entanglement to dynamically adjust quantum parameters based on the characteristics of the training data. This allows for a more robust and adaptable model, resulting in improved generalization performance. The paper details the algorithm's design, the rationale behind its proposed approach, and explores its potential impact on the field of quantum machine learning. The study emphasizes the advantages of QSAA in dynamically adjusting to data fluctuations, offering a significant advancement over existing approaches.

DOI: 10.5281/zenodo.22517676

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