Title: Quantum-Inspired Algorithms for Machine Learning

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

Quantum-inspired algorithms represent a novel approach to enhancing machine learning models, particularly in areas demanding high performance. This paper explores the design and application of quantum-inspired algorithms, drawing inspiration from quantum mechanics principles to improve the efficiency and effectiveness of various machine learning tasks. We detail a framework centered around adapting existing machine learning models – specifically neural networks – to exploit quantum phenomena such as superposition and entanglement, aiming to address current limitations in areas like reinforcement learning and generative modeling. The core mechanism revolves around incorporating these quantum concepts into the model's architecture and training process to achieve enhanced performance and robustness. This work presents a comprehensive overview of the proposed approach, highlighting the potential benefits and challenges of this emerging field.

DOI: 10.5281/zenodo.22326155

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