Quantum-Inspired Machine Learning for Signal Processing

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum-inspired machine learning (QIM) offers a novel approach to signal processing by mimicking quantum mechanical phenomena to enhance feature extraction and dimensionality reduction. This paper explores the potential of QIM to overcome limitations of classical machine learning algorithms, particularly in the context of complex signal data. We propose a new framework that leverages correlations observed in quantum systems to improve model accuracy and robustness. The core mechanism involves incorporating quantum-like elements into the learning process, potentially leading to significant improvements in performance across a range of signal processing tasks. This work presents a theoretical foundation and preliminary experimental results demonstrating the efficacy of QIM in enhancing signal analysis.

DOI: 10.5281/zenodo.22191628

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