arXiv:2604.06265v2 Announce Type: replace 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 resoluti
The paper leverages quantum-inspired techniques, a growing area of research, to address the increasing need for scalable and efficient anomaly detection in large datasets.
This development offers a potentially more efficient and scalable approach to anomaly detection, critical for various applications including cybersecurity, financial fraud, and industrial monitoring.
The proposed SMT-AD method introduces a quantum-inspired, highly parallelizable anomaly detection technique with linear parameter growth, potentially improving real-world implementation and scalability.
- · Machine Learning Researchers
- · Cybersecurity Industry
- · Financial Institutions
- · High-performance Computing
- · Inefficient Anomaly Detection Algorithms
- · Systems with Limited Parallel Processing Capabilities
Improved detection of anomalies across various sectors due to enhanced algorithmic efficiency and scalability.
Reduced operational costs and increased security or reliability in systems that adopt these quantum-inspired anomaly detection methods.
Further acceleration of quantum-inspired algorithm development and broader integration into mainstream AI applications, blurring lines between classical and quantum computing paradigms.
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Read at arXiv cs.LG