Adaptive Quantum Annealing Algorithm

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper presents an Adaptive Quantum Annealing Algorithm (AQA), designed to enhance the solution efficiency of complex optimization problems. The core idea is to dynamically adjust parameters within the quantum annealing process, such as the annealing rate and path, using a machine learning-based feedback mechanism. Specifically, a reinforcement learning algorithm is employed to monitor the quantum annealing process in real-time, analyzing its state and adjusting the annealing parameters accordingly. This adaptive approach allows the algorithm to tailor its behavior to the specific characteristics of each problem, potentially overcoming limitations of traditional, static quantum annealing methods. The algorithm's performance is evaluated through simulation, demonstrating its ability to achieve improved results compared to standard quantum annealing strategies. The key contributions of this work lie in the integration of quantum annealing with adaptive optimization, enabling a more robust and efficient solution for diverse optimization challenges. The algorithm's design provides a framework for future research in hybrid quantum-classical optimization techniques.

DOI: 10.5281/zenodo.22316223

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