Title: Quantum-Classical Hybrid Constraint Optimization for Complex Neural Networks

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum-Classical Hybrid Constraint Optimization (QCHCO) represents a novel approach to training complex neural networks by dynamically integrating quantum-enhanced exploration with classical optimization. This research investigates the potential of a hybrid algorithm that leverages quantum tunneling to navigate high-dimensional, non-convex optimization landscapes, offering a pathway to overcome current limitations in neural network design. This paper outlines the core mechanism, discusses the rationale behind its design, and presents preliminary results demonstrating the effectiveness of this hybrid optimization strategy in a specific neural network training scenario. The core claim centers on the ability to effectively explore and optimize neural network architectures with unprecedented complexity and robustness.

DOI: 10.5281/zenodo.22198545

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