Zobrazeno 1 - 10
of 135
pro vyhledávání: '"Anselmi, Fabio"'
We propose a novel approach to mitigate the decoherence effects on a target system S by coupling it to an ancillary system A with tunable parameters. By suitably engineering the S-A interaction Hamiltonian, a dark factorized compound state is found t
Externí odkaz:
http://arxiv.org/abs/2408.04418
To gain insight into the mechanisms behind machine learning methods, it is crucial to establish connections among the features describing data points. However, these correlations often exhibit a high-dimensional and strongly nonlinear nature, which m
Externí odkaz:
http://arxiv.org/abs/2406.15812
Autor:
Testa, Renzo, Rodriguez, Alex, d'Onofrio, Alberto, Trombettoni, Andrea, Benatti, Fabio, Anselmi, Fabio
Optimizing the probability of quantum tunneling between two states, while keeping the resources of the underlying physical system constant, is a task of key importance due to its critical role in various applications. We show that, by applying Machin
Externí odkaz:
http://arxiv.org/abs/2310.10165
Autor:
Testa, Renzo, Rodriguez, Alex, d'Onofrio, Alberto, Trombettoni, Andrea, Benatti, Fabio, Anselmi, Fabio
Given the key role that quantum tunneling plays in a wide range of applications, a crucial objective is to maximize the probability of tunneling from one quantum state/level to another, while keeping the resources of the underlying physical system fi
Externí odkaz:
http://arxiv.org/abs/2308.06060
Autor:
Anselmi, Fabio, Castelli, Mauro, d'Onofrio, Alberto, Manzoni, Luca, Mariot, Luca, Saletta, Martina
Geometric Semantic Geometric Programming (GSGP) is one of the most prominent Genetic Programming (GP) variants, thanks to its solid theoretical background, the excellent performance achieved, and the execution time significantly smaller than standard
Externí odkaz:
http://arxiv.org/abs/2305.16956
Autor:
Basile, Lorenzo, Karantzas, Nikos, D'Onofrio, Alberto, Bortolussi, Luca, Rodriguez, Alex, Anselmi, Fabio
Despite their impressive performance in classification tasks, neural networks are known to be vulnerable to adversarial attacks, subtle perturbations of the input data designed to deceive the model. In this work, we investigate the relation between t
Externí odkaz:
http://arxiv.org/abs/2305.15203
Convolutional neural networks (CNNs) have been employed along with Variational Monte Carlo methods for finding the ground state of quantum many-body spin systems with great success. In order to do so, however, a CNN with only linearly many variationa
Externí odkaz:
http://arxiv.org/abs/2210.00692
Autor:
Karantzas, Nikos, Besier, Emma, Caro, Josue Ortega, Pitkow, Xaq, Tolias, Andreas S., Patel, Ankit B., Anselmi, Fabio
Despite the enormous success of artificial neural networks (ANNs) in many disciplines, the characterization of their computations and the origin of key properties such as generalization and robustness remain open questions. Recent literature suggests
Externí odkaz:
http://arxiv.org/abs/2203.08822
Autor:
Caro, Josue Ortega, Ju, Yilong, Pyle, Ryan, Dey, Sourav, Brendel, Wieland, Anselmi, Fabio, Patel, Ankit
Adversarial Attacks are still a significant challenge for neural networks. Recent work has shown that adversarial perturbations typically contain high-frequency features, but the root cause of this phenomenon remains unknown. Inspired by theoretical
Externí odkaz:
http://arxiv.org/abs/2006.11440
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