Time series anomaly detection with quantum variational methods and set covering

We propose an enhanced hybrid quantum-classical framework for time series anomaly detection. Building on our previous formulation of the problem as a Quadratic Unconstrained Binary Optimization solved via the Quantum Approximate Optimization Algorithm, we extend the methodology with a statistical model selector and a refined set-covering inference scheme that accounts for temporal-value asymmetries. We analyze scalability in terms of qubit resources, assess feasibility on current quantum devices, and validate the approach on a benchmark of over one hundred heterogeneous time series. The results demonstrate interpretable decisions, robust precision, and competitive performance against both shallow classical local detectors and deep global classical architectures, highlighting the potential of the proposed methodology.