SMT-AD: a scalable quantum-inspired anomaly detection approach
This is an incremental improvement for anomaly detection tasks, offering a scalable and parallelizable method with potential feature relevance insights.
The paper tackles anomaly detection by proposing SMT-AD, a quantum-inspired tensor network approach that uses superposition of matrix product operators with Fourier-assisted embedding, achieving competitive performance on standard datasets like credit card transactions.
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.