Quantum Machine Learning: Adaptive Gradient Descent with Quantum Entanglement and Superposition

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum Machine Learning aims to leverage the unique properties of quantum mechanics to revolutionize artificial intelligence. This paper introduces a novel adaptive gradient descent algorithm incorporating quantum entanglement and superposition, promising significant acceleration in training complex models and improved accuracy. We propose a mechanism that exploits these quantum phenomena to enhance the gradient computation process, ultimately enabling faster and more effective learning. The core claim centers on the potential for quantum-enhanced gradient descent, leading to breakthroughs in machine learning applications. This work explores the theoretical underpinnings and potential practical implications of this innovative approach.

DOI: 10.5281/zenodo.22263947

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