Quantum Adaptive Algorithm Parameter Optimization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum adaptive algorithms offer the potential for significant speedups in complex computations, particularly in areas like quantum chemistry and materials science. However, achieving optimal performance often requires careful optimization of quantum parameters, a task that can be computationally expensive. This paper introduces a novel quantum adaptive algorithm that dynamically adjusts quantum parameters to optimize algorithm efficiency and accuracy, thereby reducing computational costs. We propose a method centered around a feedback loop that continuously monitors and adjusts the parameters based on the algorithm's output, leading to a more robust and adaptable optimization strategy. The core mechanism leverages a dynamic parameter tuning process to achieve this. This research explores the potential of this approach in tackling specific challenges within quantum algorithm design and provides a framework for future development. The key innovation lies in the continuous feedback loop that dynamically adjusts parameters, resulting in a more efficient and accurate algorithm.

DOI: 10.5281/zenodo.22274266

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