Quantum-Inspired Stochastic Gradient Descent with Adaptive Entanglement

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Stochastic Gradient Descent (SGD) is a cornerstone algorithm in modern machine learning, yet it often encounters challenges in navigating complex, non-convex optimization landscapes. Traditional SGD methods predominantly focus on iteratively refining the gradient estimate, which can lead to premature convergence to local minima or saddle points. This paper introduces a novel approach, Quantum-Inspired Stochastic Gradient Descent with Adaptive Entanglement (QISGD-AE), designed to mitigate these limitations. QISGD-AE draws inspiration from quantum superposition and entanglement, dynamically adjusting the "entanglement" between nodes in the network during the optimization process. This adaptive entanglement facilitates a more comprehensive exploration of the solution space, enhancing the algorithm's ability to escape local optima and converge to globally optimal or near-optimal solutions. The core mechanism involves a stochastic probabilistic adjustment of node relationships, effectively mimicking the parallel exploration inherent in quantum systems. We present a detailed formulation of the algorithm, including key equations and parameters, demonstrating its potential for improved performance in various non-convex optimization tasks. The experimental results, though focusing on conceptual demonstration, highlight the algorithm's capacity to outperform standard SGD in specific scenarios, offering a promising direction for future research in adaptive optimization.

DOI: 10.5281/zenodo.22152423

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