Quantum-Inspired Constraint Optimization for Neural Network Design

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum-inspired constraint optimization (QICO) offers a promising approach to neural network architecture design, leveraging quantum mechanical principles to explore a broader solution space than traditional methods. This paper introduces a novel QICO framework utilizing quantum annealing and variational quantum eigensolver (VQE) to optimize network configurations. We demonstrate the effectiveness of this approach through a series of simulations, showcasing the potential for discovering innovative network architectures with improved performance. The core mechanism involves quantum-enhanced exploration of the solution space, addressing limitations of classical optimization techniques.

DOI: 10.5281/zenodo.22222968

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