Hybrid Quantum-Classical Framework for Anomaly Detection in Time Series with QUBO formulation and QAOA

In this work, we introduce a hybrid quantum-classical framework to address anomaly detection problems in time series data using an innovative Quadratic Unconstrained Binary Optimization formulation. The proposed approach integrates density-based and statistical methodologies with the Quantum Approximate Optimization Algorithm, providing a versatile tool for anomaly detection. Moreover, the underlying model is transversal to different kinds of anomalies and can be directly applied to diverse applications, ranging from fault detection to novelty discovery. Experimental results prove that this architecture achieves competitive accuracy compared to classical techniques, with the unique advantage of exploring alternative solutions in parallel due to the properties of quantum computing. This capability enables a deeper understanding of patterns in time series data.