Title: Quantum-Inspired Optimization via Stochastic Gradient Descent

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum-inspired optimization represents a promising avenue for addressing computationally challenging optimization problems. This paper investigates the application of stochastic gradient descent (SGD) to emulate the quantum annealing process, aiming to improve the speed and accuracy of complex optimization tasks. We propose a novel algorithm that iteratively refines a solution based on the gradient of the objective function, mimicking the probabilistic exploration inherent in quantum annealing. This approach offers a potentially more efficient and scalable method compared to traditional optimization techniques, leveraging the strengths of stochasticity. The core mechanism centers around utilizing SGD to dynamically adjust the solution trajectory, effectively mimicking the quantum annealing process.

DOI: 10.5281/zenodo.22192913

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