@article{Sgroi2024,

title = {Efficient excitation-transfer across fully connected networks via local-energy optimization},

author = {S. Sgroi and G. Zicari and A. Imparato and M. Paternostro},

doi = {10.1140/epjqt/s40507-024-00238-w},

issn = {2196-0763},

year  = {2024},

date = {2024-12-00},

journal = {EPJ Quantum Technol.},

volume = {11},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {AbstractWe study the excitation transfer across a fully connected quantum network whose sites energies can be artificially designed. Starting from a simplified model of a broadly-studied physical system, we systematically optimize its local energies to achieve high excitation transfer for various environmental conditions, using an adaptive Gradient Descent technique and Automatic Differentiation. We show that almost perfect transfer can be achieved with and without local dephasing, provided that the dephasing rates are not too large. We investigate our solutions in terms of resilience against variations in either the network connection strengths, or size, as well as coherence losses. We highlight the different features of a dephasing-free and dephasing-driven transfer. Our work gives further insight into the interplay between coherence and dephasing effects in excitation-transfer phenomena across fully connected quantum networks. In turn, this will help designing optimal transfer in artificial open networks through the simple manipulation of local energies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DiBartolomeo2024,

title = {Experimental bounds on linear-friction dissipative collapse models from levitated optomechanics},

author = {Giovanni Di Bartolomeo and Matteo Carlesso},

doi = {10.1088/1367-2630/ad3842},

issn = {1367-2630},

year  = {2024},

date = {2024-04-01},

journal = {New J. Phys.},

volume = {26},

number = {4},

publisher = {IOP Publishing},

abstract = {Abstract

               Collapse models constitute an alternative to quantum mechanics that solve the well-know quantum measurement problem. In this framework, a novel approach to include dissipation in collapse models has been recently proposed, and awaits experimental scrutiny. Our work establishes experimental bounds on the so-constructed linear-friction dissipative Diósi-Penrose (dDP) and Continuous Spontaneous localisation (dCSL) models by exploiting experiments in the field of levitated optomechanics. Our results in the dDP case exclude collapse temperatures below 10−13 K and 

                     

                     

                        

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                   K respectively for values of the localisation length smaller than 10−6 m and 10−8 m. In the dCSL case the entire parameter space is excluded for values of the temperature lower than 

                     

                     

                        

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                   K.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Suprano2024,

title = {Experimental Property Reconstruction in a Photonic Quantum Extreme Learning Machine},

author = {Alessia Suprano and Danilo Zia and Luca Innocenti and Salvatore Lorenzo and Valeria Cimini and Taira Giordani and Ivan Palmisano and Emanuele Polino and Nicolò Spagnolo and Fabio Sciarrino and G. Massimo Palma and Alessandro Ferraro and Mauro Paternostro},

doi = {10.1103/physrevlett.132.160802},

issn = {1079-7114},

year  = {2024},

date = {2024-04-00},

journal = {Phys. Rev. Lett.},

volume = {132},

number = {16},

publisher = {American Physical Society (APS)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Barr2024,

title = {Spectral density classification for environment spectroscopy},

author = {J Barr and G Zicari and A Ferraro and M Paternostro},

doi = {10.1088/2632-2153/ad2cf1},

issn = {2632-2153},

year  = {2024},

date = {2024-03-01},

journal = {Mach. Learn.: Sci. Technol.},

volume = {5},

number = {1},

publisher = {IOP Publishing},

abstract = {Abstract

               Spectral densities encode the relevant information characterizing the system–environment interaction in an open-quantum system problem. Such information is key to determining the system’s dynamics. In this work, we leverage the potential of machine learning techniques to reconstruct the features of the environment. Specifically, we show that the time evolution of a system observable can be used by an artificial neural network to infer the main features of the spectral density. In particular, for relevant examples of spin-boson models, we can classify with high accuracy the Ohmicity parameter of the environment as either Ohmic, sub-Ohmic or super-Ohmic, thereby distinguishing between different forms of dissipation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}