Quantum-Inspired Anomaly Detection via Tensor Decomposition

Jincheng Zhang

Published Aug 28, 2026• 0 citations

Abstract

This paper investigates the application of quantum-inspired principles to enhance anomaly detection using tensor decomposition. Traditional tensor decomposition methods, while effective, often struggle to fully capture the complex, non-linear relationships inherent in high-dimensional data, particularly when dealing with subtle anomalies. We propose a novel approach that leverages concepts from quantum mechanics, specifically tensor train (TT) representations with entanglement-like constraints, to improve anomaly detection performance. The core idea is that anomalies, by their very nature, represent deviations from the expected tensor structure. By incorporating constraints mimicking quantum entanglement, we aim to create tensor representations that are more sensitive to these deviations. We demonstrate, through theoretical analysis and conceptual development, the potential of this approach to improve anomaly detection accuracy. The methodology presented here offers a new perspective on tensor decomposition, moving beyond purely classical approaches and opening avenues for more robust and sensitive anomaly detection in diverse datasets. The focus is on the conceptual framework and the potential benefits rather than empirical validation due to the absence of experimental data.

DOI: 10.5281/zenodo.22140223

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