Neural network-powered systems on classical computers face scalability and efficiency challenges. Here, the authors introduce a quantum reservoir network algorithm that leverages current quantum hardware to predict and reconstruct dynamical systems, demonstrating enhanced memory persistence and state-of-the-art time-series performance on IBM quantum processors, with significant implications for quantum computing.
- Erik L. Connerty
- Ethan N. Evans
- Vignesh Narayanan
