Zobrazeno 1 - 10
of 8 533
pro vyhledávání: '"Pavesi A"'
Autor:
Biasi, Stefano, Foradori, Alessandro, Franchi, Riccardo, Lugnan, Alessio, Bienstman, Peter, Pavesi, Lorenzo
While biological neurons ensure unidirectional signalling, scalable integrated photonic neurons, such as silicon microresonators, respond the same way regardless of excitation direction due to the Lorentz reciprocity principle. Here, we show that a n
Externí odkaz:
http://arxiv.org/abs/2410.03257
A feed-forward photonic neural network (PNN) is tested for chromatic dispersion compensation in Intensity Modulation/Direct Detection optical links. The PNN is based on a sequence of linear and nonlinear transformations. The linear stage is constitut
Externí odkaz:
http://arxiv.org/abs/2409.03547
Autor:
Baldazzi, Alessio, Pavesi, Lorenzo
We show how to use Boson Sampling schemes in order to create photonic post-selected Controlled-Z and Controlled-Controlled-Z gates, which are equivalent, modulo single-qubit gates, to Controlled-NOT and Toffoli gates, respectively. The new proposed m
Externí odkaz:
http://arxiv.org/abs/2408.02832
Autor:
Angileri, Flora, Lombardi, Giulia, Fois, Andrea, Faraone, Renato, Metta, Carlo, Salvi, Michele, Bianchi, Luigi Amedeo, Fantozzi, Marco, Galfrè, Silvia Giulia, Pavesi, Daniele, Parton, Maurizio, Morandin, Francesco
In 2021, Adam Zsolt Wagner proposed an approach to disprove conjectures in graph theory using Reinforcement Learning (RL). Wagner's idea can be framed as follows: consider a conjecture, such as a certain quantity f(G) < 0 for every graph G; one can t
Externí odkaz:
http://arxiv.org/abs/2406.12667
Publikováno v:
Phys. Rev. E 110 (2024), 054304
Network systems can exhibit memory effects in which the interactions between different pairs of nodes adapt in time, leading to the emergence of preferred connections, patterns, and sub-networks. To a first approximation, this memory can be modelled
Externí odkaz:
http://arxiv.org/abs/2403.13967
Among supervised learning models, Support Vector Machine stands out as one of the most robust and efficient models for classifying data clusters. At the core of this method, a kernel function is employed to calculate the distance between different el
Externí odkaz:
http://arxiv.org/abs/2402.17923
Publikováno v:
Opt. Express 32, 12852-12881 (2024)
Linear optical quantum computing (LOQC) offers a quantum computation paradigm based on well-established and robust technology and flexible environmental conditions following DiVincenzo's criteria. Within this framework, integrated photonics can be ut
Externí odkaz:
http://arxiv.org/abs/2401.16875
Autor:
Ye, Kaixuan, Keloth, Akshay, Klaver, Yvan, Baldazzi, Alessio, Piccoli, Gioele, Sanna, Matteo, Pavesi, Lorenzo, Ghulinyan, Mher, Marpaung, David
Silicon oxynitride (SiON) is a low-loss and versatile material for linear and nonlinear photonics applications. Controlling the oxygen-to-nitrogen (O/N) ratio in SiON provides an effective way to engineer its optical and mechanical properties, making
Externí odkaz:
http://arxiv.org/abs/2401.12651
Autor:
Sanna, Matteo, Baldazzi, Alessio, Piccoli, Gioele, Azzini, Stefano, Ghulinyan, Mher, Pavesi, Lorenzo
Publikováno v:
Optics Express Vol. 32, Issue 6, pp. 9081-9094 (2024)
Integrated photonics has emerged as one of the most promising platforms for quantum applications. The performances of quantum photonic integrated circuits (QPIC) necessitate a demanding optimization to achieve enhanced properties and tailored charact
Externí odkaz:
http://arxiv.org/abs/2311.16016
In this study, we address the challenge of analyzing electrophysiological measurements in neuronal networks. Our computational model, based on the Reservoir Computing Network (RCN) architecture, deciphers spatio-temporal data obtained from electrophy
Externí odkaz:
http://arxiv.org/abs/2311.03131