SMT-AD: a scalable quantum-inspired anomaly detection approach

2026-04-06

AI Summary

This work addresses the limited efficiency and scalability of anomaly detection in high-dimensional data by proposing a quantum-inspired approach based on multi-resolution tensor superposition. The method integrates Fourier-assisted feature embedding with a low-rank matrix product operator architecture to enable efficient modeling: its parameter count grows linearly with feature dimensionality, supports high parallelizability, and automatically emphasizes salient features. Evaluated on standard benchmarks such as credit card transaction datasets, the proposed approach achieves detection performance comparable to or better than state-of-the-art baselines while substantially reducing model complexity, thereby offering a lightweight yet highly scalable solution for high-dimensional anomaly detection.

Abstract

Quantum-inspired tensor networks algorithms have shown to be effective and efficient models for machine learning tasks, including anomaly detection. Here, we propose a highly parallelizable quantum-inspired approach which we call SMT-AD from Superposition of Multiresolution Tensors for Anomaly Detection. It is based upon the superposition of bond-dimension-1 matrix product operators to transform the input data with Fourier-assisted feature embedding, where the number of learnable parameters grows linearly with feature size, embedding resolutions, and the number of additional components in the matrix product operators structure. We demonstrate successful anomaly detection when applied to standard datasets, including credit card transactions, and find that, even with minimal configurations, it achieves competitive performance against established anomaly detection baselines. Furthermore, it provides a straightforward way to reduce the weight of the model and even improve the performance by highlighting the most relevant input features.

Problem

• anomaly detection
• quantum-inspired
• tensor networks
• scalability
• feature embedding

Innovation

• quantum-inspired
• tensor networks
• anomaly detection
• matrix product operators
• Fourier feature embedding