3 citations · 3 across the 2 of their papers we have counts for
7 papers
Online quantum time series processing with random oscillator networks
Johannes Nokkala
Reservoir computing is a powerful machine learning paradigm for online time series processing. It has reached state-of-the-art performance in tasks such as chaotic time series pred…
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…
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…
Probing the spectral dimension of quantum network geometries
Johannes Nokkala, Jyrki Piilo, Ginestra Bianconi
We consider an environment for an open quantum system described by a "Quantum Network Geometry with Flavor" (QNGF) in which the nodes are coupled quantum oscillators. The geometric…