Dynamic Quantum Algorithm Optimization (DQAO)

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces the Dynamic Quantum Algorithm Optimization (DQAO) framework, a novel approach to quantum algorithm optimization that goes beyond traditional static optimization techniques. DQAO dynamically adapts to the quantum algorithm's internal state and incorporates its inherent properties to significantly enhance algorithm efficiency. The core mechanism revolves around leveraging the quantum algorithm's own quantum properties, specifically its superposition and entanglement, to refine the optimization process. This allows for a more responsive and effective optimization, achieving improved performance compared to conventional methods, particularly in scenarios involving complex quantum circuits. We present a detailed theoretical analysis and preliminary experimental results demonstrating the effectiveness of this approach.

DOI: 10.5281/zenodo.22486775

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