Zobrazeno 1 - 10
of 160
pro vyhledávání: '"Pena, Rodrigo"'
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 makes a quantum reservoir valuable: contractive dynamics of the quant
Externí odkaz:
http://arxiv.org/abs/2412.08322
We address the problem of identifying functional interactions among stochastic neurons with variable-length memory from their spiking activity. The neuronal network is modeled by a stochastic system of interacting point processes with variable-length
Externí odkaz:
http://arxiv.org/abs/2411.08205
Autor:
Palacios, Ana, Martínez-Peña, Rodrigo, Soriano, Miguel C., Giorgi, Gian Luca, Zambrini, Roberta
Quantum Reservoir Computing (QRC) offers potential advantages over classical reservoir computing, including inherent processing of quantum inputs and a vast Hilbert space for state exploration. Yet, the relation between the performance of reservoirs
Externí odkaz:
http://arxiv.org/abs/2409.17734
Autor:
De Gregorio, Giuseppe, Perrin, Simon, Pena, Rodrigo C. G., Marthot-Santaniello, Isabelle, Mouchère, Harold
The intersection of computer vision and machine learning has emerged as a promising avenue for advancing historical research, facilitating a more profound exploration of our past. However, the application of machine learning approaches in historical
Externí odkaz:
http://arxiv.org/abs/2408.07785
Autor:
De Gregorio, Giuseppe, Ferretti, Lavinia, Pena, Rodrigo C. G., Marthot-Santaniello, Isabelle, Konstantinidou, Maria, Pavlopoulos, John
The study of Greek papyri from ancient Egypt is fundamental for understanding Graeco-Roman Antiquity, offering insights into various aspects of ancient culture and textual production. Palaeography, traditionally used for dating these manuscripts, rel
Externí odkaz:
http://arxiv.org/abs/2408.07779
Publikováno v:
Physical Review A 109 (4), 042612 (2024)
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 generalization capability, and the scalability of quantum kernels becomes a
Externí odkaz:
http://arxiv.org/abs/2311.18719
Autor:
Sannia, Antonio, Martínez-Peña, Rodrigo, Soriano, Miguel C., Giorgi, Gian Luca, Zambrini, Roberta
Publikováno v:
Quantum 8, 1291 (2024)
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. We show the potential enhancement induced by dissipation in the field
Externí odkaz:
http://arxiv.org/abs/2212.12078
Publikováno v:
Physical Review E (2023), 107 (3), 035306
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 that alternative representations can provide better insights when dealing
Externí odkaz:
http://arxiv.org/abs/2212.00396
Autor:
Mujal, Pere, Martínez-Peña, Rodrigo, Giorgi, Gian Luca, Soriano, Miguel C., Zambrini, Roberta
Publikováno v:
npj Quantum Inf 9, 16 (2023)
Quantum machine learning represents a promising avenue for data processing, also for purposes of sequential temporal data analysis, as recently proposed in quantum reservoir computing (QRC). The possibility to operate on several platforms and noise i
Externí odkaz:
http://arxiv.org/abs/2205.06809
Autor:
Shimoura, Renan Oliveira, Pena, Rodrigo F. O., Lima, Vinicius, Kamiji, Nilton L., Girardi-Schappo, Mauricio, Roque, Antonio C.
Publikováno v:
Eur. Phys. J. Spec. Top. (2021)
The field of computational modeling of the brain is advancing so rapidly that now it is possible to model large scale networks representing different brain regions with a high level of biological detail in terms of numbers and synapses. For a theoret
Externí odkaz:
http://arxiv.org/abs/2106.03995