91 citations · 154 across the 7 of their papers we have counts for
7 papers
Feedback-driven recurrent quantum neural network universality
Lukas Gonon, Rodrigo Martínez-Peña, Juan-Pablo Ortega
Quantum reservoir computing uses the dynamics of quantum systems to process temporal data, making it particularly well-suited for machine learning with noisy intermediate-scale qua…
A tensor network approach for chaotic time series prediction
Rodrigo Martínez-Peña, Román Orús
Making accurate predictions of chaotic time series is a complex challenge. Reservoir computing, a neuromorphic-inspired approach, has emerged as a powerful tool for this task. It e…
Input-dependence in quantum reservoir computing
Rodrigo Martínez-Peña, Juan-Pablo Ortega
Quantum reservoir computing is an emergent field in which quantum dynamical systems are exploited for temporal information processing. In previous work, it was found a feature that…
Role of coherence in many-body Quantum Reservoir Computing
Ana Palacios, Rodrigo Martínez-Peña, Miguel C. Soriano +2
Quantum Reservoir Computing (QRC) offers potential advantages over classical reservoir computing, including inherent processing of quantum inputs and a vast Hilbert space for state…
Quantum fidelity kernel with a trapped-ion simulation platform
Rodrigo Martínez-Peña, Miguel C. Soriano, Roberta Zambrini
Quantum kernel methods leverage a kernel function computed by embedding input information into the Hilbert space of a quantum system. However, large Hilbert spaces can hinder gener…
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.…
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…