Title: Adaptive Constraint Optimization via Quantum Annealing & Hybrid Neural Networks

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Adaptive constraint optimization is a critical challenge in various fields, particularly in areas like engineering design, logistics, and finance, where finding optimal solutions within complex, high-dimensional spaces is essential. Traditional methods often struggle with such problems, particularly when the solution space is intricate and the constraints are non-trivial. This paper proposes a novel algorithm combining quantum annealing and a neural network to address this challenge. We introduce a hybrid approach where a neural network dynamically adjusts constraint parameters during the optimization process, leveraging the quantum annealing process to efficiently explore the solution space. This hybrid method promises significant speed and accuracy improvements over existing techniques, offering a promising pathway towards more efficient and robust optimization solutions. The core mechanism focuses on leveraging quantum annealing's ability to efficiently search sparse solution spaces, coupled with the neural network's capacity to learn and adapt to the problem's characteristics. The paper details the algorithm's design, its performance evaluation, and potential applications.

DOI: 10.5281/zenodo.22186865

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