Adaptive Quantum State Optimization

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper introduces an innovative algorithm for quantum state optimization, leveraging the inherent adaptability of quantum states. Traditional approaches to optimizing quantum states often involve manual parameter tuning, which can be time-consuming and limit scalability. Our algorithm utilizes a dynamic parameter optimization technique, intelligently adjusting quantum state parameters to achieve improved efficiency and accuracy. The core mechanism centers around exploiting the quantum state's self-correcting properties to autonomously search for optimal configurations. This paper details the algorithm's design, implementation, and preliminary results, demonstrating significant improvements over existing methods in a specific quantum computing benchmark. The focus is on automating the process of parameter adjustment, offering a pathway to more efficient and accurate quantum computations.

DOI: 10.5281/zenodo.22244309

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