Nonlinear Quantum Self-Adaptive Learning

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper presents a novel approach to quantum machine learning, termed Nonlinear Quantum Self-Adaptive Learning (NQ-SSL). The core principle of NQ-SSL leverages a dynamically adjusted quantum parameter set, achieved through a self-adaptive learning algorithm, to optimize the performance of quantum systems. Traditional quantum learning methods often require extensive manual parameter tuning, limiting the adaptability of quantum algorithms to complex, real-world scenarios. NQ-SSL offers a paradigm shift by allowing the system to learn optimal parameters automatically, leading to enhanced system performance and robust self-adaptation. This work introduces a novel quantum self-adaptation mechanism based on a feedback loop, enhancing the algorithm's ability to navigate and optimize quantum states. We demonstrate the efficacy of NQ-SSL through simulations illustrating improved convergence speed and enhanced performance across a range of quantum benchmark problems.

DOI: 10.5281/zenodo.22637104

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