71 citations · 83 across the 3 of their papers we have counts for
8 papers
Nonlinear photonic dynamical systems for unconventional computing
Daniel Brunner, Laurent Larger, Miguel C. Soriano
Driven by the remarkable breakthroughs during the past decade, photonics neural networks have experienced a revival. Here, we provide a general overview of progress over the past d…
Dynamical phase transitions in quantum reservoir computing
Rodrigo Martínez-Peña, Gian Luca Giorgi, Johannes Nokkala +2
Closed quantum systems exhibit different dynamical regimes, like Many-Body Localization or thermalization, which determine the mechanisms of spread and processing of information. H…
Opportunities in Quantum Reservoir Computing and Extreme Learning Machines
Pere Mujal, Rodrigo Martínez-Peña, Johannes Nokkala +4
Quantum reservoir computing (QRC) and quantum extreme learning machines (QELM) are two emerging approaches that have demonstrated their potential both in classical and quantum mach…
Unveiling the role of plasticity rules in reservoir computing
Guillermo B. Morales, Claudio R. Mirasso, Miguel C. Soriano
Reservoir Computing (RC) is an appealing approach in Machine Learning that combines the high computational capabilities of Recurrent Neural Networks with a fast and easy training m…
Information Processing Capacity of Spin-Based Quantum Reservoir Computing Systems
R. Martínez-Peña, J. Nokkala, G. L. Giorgi +2
The dynamical behaviour of complex quantum systems can be harnessed for information processing. With this aim, quantum reservoir computing (QRC) with Ising spin networks was recent…
Gaussian states of continuous-variable quantum systems provide universal and versatile reservoir computing
Johannes Nokkala, Rodrigo Martínez-Peña, Gian Luca Giorgi +3
We establish the potential of continuous-variable Gaussian states of linear dynamical systems for machine learning tasks. Specifically, we consider reservoir computing, an efficien…