165 citations · 1k across the 16 of their papers we have counts for
16 papers
Dissipation as a resource for Quantum Reservoir Computing
Antonio Sannia, Rodrigo Martínez-Peña, Miguel C. Soriano +2
Dissipation induced by interactions with an external environment typically hinders the performance of quantum computation, but in some cases can be turned out as a useful resource.…
Scalable photonic platform for real-time quantum reservoir computing
Jorge García-Beni, Gian Luca Giorgi, Miguel C. Soriano +1
Quantum Reservoir Computing (QRC) exploits the information processing capabilities of quantum systems to solve non-trivial temporal tasks, improving over their classical counterpar…
Time Series Quantum Reservoir Computing with Weak and Projective Measurements
Pere Mujal, Rodrigo Martínez-Peña, Gian Luca Giorgi +2
Quantum machine learning represents a promising avenue for data processing, also for purposes of sequential temporal data analysis, as recently proposed in quantum reservoir comput…
Learn one size to infer all: Exploiting translational symmetries in delay-dynamical and spatio-temporal systems using scalable neural networks
Mirko Goldmann, Claudio R. Mirasso, Ingo Fischer +1
We design scalable neural networks adapted to translational symmetries in dynamical systems, capable of inferring untrained high-dimensional dynamics for different system sizes. We…
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…
Machine learning algorithms for predicting the amplitude of chaotic laser pulses
Pablo Amil, Miguel C. Soriano, Cristina Masoller
Forecasting the dynamics of chaotic systems from the analysis of their output signals is a challenging problem with applications in most fields of modern science. In this work, we…
Machine learning applied to quantum synchronization-assisted probing
Gabriel Garau Estarellas, Gian Luca Giorgi, Miguel C. Soriano +1
A probing scheme is considered with an accessible and controllable qubit, used to probe an out-of equilibrium system consisting of a second qubit interacting with an environment. Q…
Photonic Delay Systems as Machine Learning Implementations
Michiel Hermans, Miguel Soriano, Joni Dambre +2
Nonlinear photonic delay systems present interesting implementation platforms for machine learning models. They can be extremely fast, offer great degrees of parallelism and potent…
Reservoir computing with a single time-delay autonomous Boolean node
Nicholas D. Haynes, Miguel C. Soriano, David P. Rosin +2
We demonstrate reservoir computing with a physical system using a single autonomous Boolean logic element with time-delay feedback. The system generates a chaotic transient with a…
Strong and Weak Chaos in Networks of Semiconductor Lasers with Time-delayed Couplings
Sven Heiligenthal, Thomas Jüngling, Otti D'Huys +5
Nonlinear networks with time-delayed couplings may show strong and weak chaos, depending on the scaling of their Lyapunov exponent with the delay time. We study strong and weak cha…
Assessing coupling dynamics from an ensemble of time series
German Gomez-Herrero, Wei Wu, Kalle Rutanen +3
Finding interdependency relations between (possibly multivariate) time series provides valuable knowledge about the processes that generate the signals. Information theory sets a n…