activity
20202026
most citedOpportunities in Quantum Reservoir Computing and Extreme Learning Machines

165 citations · 474 across the 7 of their papers we have counts for

collaborators

7 papers

nlin.AO2026

Reconstructing the information processing capacity of physical systems from noisy observations

Shun Kotoku, Rodrigo Martínez-Peña, Takatomo Mihana +5

Driven dynamical systems can compute when their transient states encode complex transformations of past inputs. The information processing capacity (IPC) framework allows for a det…

quant-ph2026

Measurement-enabled online quantum processing with amplitude encoding

Giacomo Franceschetto, Pere Mujal, Rodrigo Martínez-Peña

We introduce a quantum reservoir computing online protocol that realizes amplitude encoding on quantum hardware. Our scheme combines mid-circuit measurement and reset operations to…

quant-ph2022★ 29 cited

Quantum reservoir computing in finite dimensions

Rodrigo Martínez-Peña, Juan-Pablo Ortega

Most existing results in the analysis of quantum reservoir computing (QRC) systems with classical inputs have been obtained using the density matrix formalism. This paper shows tha…

quant-ph2022★ 94 cited

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…

quant-ph2021★ 116 cited

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…

quant-ph2021★ 165 cited

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…

quant-ph2020★ 70 cited

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…