Adaptive Quantum Annealing for Constraint Satisfaction Problems

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Constraint Satisfaction Problems (CSPs) represent a significant class of computationally challenging problems across diverse fields, including scheduling, routing, and artificial intelligence. Traditional quantum annealing (QA) approaches often face difficulties in effectively tackling complex CSPs, primarily due to challenges in accurately embedding the problem's structure onto the quantum hardware and efficiently exploring the solution space. This research proposes an adaptive quantum annealing (AQA) algorithm designed to mitigate these limitations. The AQA algorithm employs reinforcement learning (RL) to dynamically adjust the embedding strategy and refine the annealing schedule during the QA process. Specifically, the RL agent learns to optimize the problem's representation on the quantum hardware and adapt the annealing parameters based on real-time feedback from the observed solution landscape. The core innovation lies in the integration of adaptive control within the QA framework, potentially unlocking significant improvements in performance for complex CSPs. We present a theoretical framework and outline the key components of the AQA algorithm, demonstrating its potential to overcome the limitations associated with conventional QA methods. The algorithm's ability to dynamically respond to the problem's characteristics offers a promising avenue for enhancing the effectiveness of QA in solving challenging CSP instances.

DOI: 10.5281/zenodo.22273959

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