Quantum-Walk Based Optimization of Neural Network Weights

Jincheng Zhang

Published Aug 28, 2026• 0 citations

Abstract

This paper investigates the potential of quantum walks for optimizing the weights within neural networks. Traditional neural network training relies heavily on classical gradient-based optimization methods, which can often get trapped in local minima and suffer from slow convergence rates. We propose a novel approach leveraging the inherent properties of quantum walks to efficiently explore the complex solution space of neural network weight optimization. The core idea involves mapping the weight update process to a quantum walk, where each walker represents a candidate weight adjustment. The walk's evolution is determined by a Hamiltonian operator constructed from the neural network's loss function. We aim to demonstrate that, under specific conditions, quantum walks can outperform classical gradient descent in terms of convergence speed and potentially escape local minima more effectively. This research represents a novel combination of quantum walk methodology with neural network optimization, offering a fundamentally different paradigm for training complex artificial intelligence models.

DOI: 10.5281/zenodo.22140031

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