Hybrid Quantum-Classical Framework for Anomaly Detection in Time Series with QUBO formulation and QAOA
Published in 2025 International Joint Conference on Neural Networks (IJCNN), 2025
Recommended citation: M. Casalbore, L. Lavagna, A. Rosato and M. Panella, "Hybrid Quantum-Classical Framework for Anomaly Detection in Time Series with QUBO formulation and QAOA," 2025 International Joint Conference on Neural Networks (IJCNN), Rome, Italy, 2025, pp. 1-8. https://ieeexplore.ieee.org/document/11228152
In this work, we introduce new quantum machine learning models that combine both quantum and hyperdimensional computing. We focus our effort on two novel architectures that are first theoretically demonstrated, and then applied for testing to prototypical machine learning tasks, namely for pattern completion, classification, and clustering. We obtained accurate and promising results that prove, for the first time, the synergies between two of the most innovative computational approaches such as quantum computing and hyperdimensional computing.