Adaptive Quantum Annealing Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper proposes a novel approach to quantum annealing optimization by integrating quantum annealing's inherent properties with machine learning-driven adaptive parameter tuning. The core idea revolves around a machine learning agent – typically a reinforcement learning model – which dynamically monitors the quantum annealing process, specifically tracking parameters such as amplitude, time scale, and the transfer matrix. Based on the observed characteristics of the optimization problem, the agent autonomously adjusts these parameters in real-time, aiming to significantly improve both the efficiency and accuracy of solving complex optimization problems. This adaptive methodology contrasts with traditional quantum annealing, which relies on pre-defined parameters, offering increased flexibility and adaptability. We demonstrate a theoretical framework and a conceptual outline of this adaptive quantum annealing algorithm, highlighting its potential advantages and future research directions. The key mathematical formulations and notations used throughout the paper are presented in plain text.

DOI: 10.5281/zenodo.22202555

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