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

TL;DR

SMT-AD is a scalable, quantum-inspired anomaly detection method leveraging tensor networks and Fourier features, demonstrating competitive results on standard datasets with minimal configurations.

Contribution

It introduces a novel, highly parallelizable quantum-inspired tensor network approach for anomaly detection, emphasizing scalability and feature relevance.

Findings

Achieves competitive anomaly detection performance on standard datasets.

Allows model size reduction and performance improvement by emphasizing relevant features.

Demonstrates effectiveness with minimal configurations.

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.