Adaptive Self-Organized Quantum Computation Algorithm Optimization

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum computation is rapidly advancing, demanding sophisticated algorithms and efficient optimization techniques. This paper explores the integration of adaptive self-organized quantum computation into algorithm optimization. We propose a novel framework leveraging self-organizing quantum systems to dynamically adjust parameters of quantum algorithms, resulting in enhanced performance. This approach addresses the limitations of traditional optimization methods by adapting to the algorithm's unique characteristics in real-time. The core mechanism involves the quantum system itself acting as a self-learning optimization tool. The design is predicated on the observation that the optimal parameter settings for quantum algorithms are rarely static; instead, they evolve based on the algorithm's trajectory. This paper details the design, implementation, and preliminary results demonstrating the effectiveness of this adaptive framework for optimizing a specific quantum algorithm. The paper concludes with a discussion of future research directions and potential applications.

DOI: 10.5281/zenodo.22246941

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