Zobrazeno 1 - 10
of 1 230
pro vyhledávání: '"A. Ceschini"'
Autor:
Bergadano, Lorenzo, Ceschini, Andrea, Chiavassa, Pietro, Giusto, Edoardo, Montrucchio, Bartolomeo, Panella, Massimo, Rosato, Antonello
In a world burdened by air pollution, the integration of state-of-the-art sensor calibration techniques utilizing Quantum Computing (QC) and Machine Learning (ML) holds promise for enhancing the accuracy and efficiency of air quality monitoring syste
Externí odkaz:
http://arxiv.org/abs/2410.02998
We investigate the Maximum Cut (MaxCut) problem on different graph classes with the Quantum Approximate Optimization Algorithm (QAOA) using symmetries. In particular, heuristics on the relationship between graph symmetries and the approximation ratio
Externí odkaz:
http://arxiv.org/abs/2408.15413
Autor:
Ceschini, Andrea, Mauro, Francesco, De Falco, Francesca, Sebastianelli, Alessandro, Verdone, Alessio, Rosato, Antonello, Saux, Bertrand Le, Panella, Massimo, Gamba, Paolo, Ullo, Silvia L.
Quantum Graph Neural Networks (QGNNs) represent a novel fusion of quantum computing and Graph Neural Networks (GNNs), aimed at overcoming the computational and scalability challenges inherent in classical GNNs that are powerful tools for analyzing da
Externí odkaz:
http://arxiv.org/abs/2408.06524
Autor:
Piperno, Simone, Ceschini, Andrea, Chang, Su Yeon, Grossi, Michele, Vallecorsa, Sofia, Panella, Massimo
This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approach produces similar outcomes with respect to previous models but with
Externí odkaz:
http://arxiv.org/abs/2408.03427
Autor:
De Falco, Francesca, Ceschini, Andrea, Sebastianelli, Alessandro, Saux, Bertrand Le, Panella, Massimo
Publikováno v:
KI - K\"unstliche Intelligenz, pp. 1-16, ISSN: 0933-1875, Springer Nature, Germania, August, 2024
In this paper, we propose a new methodology to design quantum hybrid diffusion models, derived from classical U-Nets with ResNet and Attention layers. Specifically, we propose two possible different hybridization schemes combining quantum computing's
Externí odkaz:
http://arxiv.org/abs/2402.16147
Publikováno v:
Adv Quantum Technol. 2023, 2300220
In classical Machine Learning, "overfitting" is the phenomenon occurring when a given model learns the training data excessively well, and it thus performs poorly on unseen data. A commonly employed technique in Machine Learning is the so called "dro
Externí odkaz:
http://arxiv.org/abs/2310.04120
This paper proposes a new method for determining the simulation parameters of the Jiles-Atherton Model used to simulate the first magnetization curve and hysteresis loop in ferromagnetic materials. The Jiles-Atherton Model is an important tool in eng
Externí odkaz:
http://arxiv.org/abs/2308.14573
Autor:
Blekos, Kostas, Brand, Dean, Ceschini, Andrea, Chou, Chiao-Hui, Li, Rui-Hao, Pandya, Komal, Summer, Alessandro
The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising variational quantum algorithm that aims to solve combinatorial optimization problems that are classically intractable. This comprehensive review offers an overview of the cur
Externí odkaz:
http://arxiv.org/abs/2306.09198
Autor:
Incudini, Massimiliano, Grossi, Michele, Ceschini, Andrea, Mandarino, Antonio, Panella, Massimo, Vallecorsa, Sofia, Windridge, David
Publikováno v:
Quantum Machine Intelligence, vol. 5, no. 2, pp. 1-24, ISSN: 2524-4906, Springer Nature, Germany, December 2023
Quantum neural networks hold significant promise for numerous applications, particularly as they can be executed on the current generation of quantum hardware. However, due to limited qubits or hardware noise, conducting large-scale experiments often
Externí odkaz:
http://arxiv.org/abs/2303.11283
Autor:
Filipe Zimmer Dezordi, José Valter Joaquim Silva Júnior, Terimar Facin Ruoso, Angela Giovana Batista, Pedro Mesquita Fonseca, Larissa Paim Bernardo, Richard Steiner Salvato, Tatiana Schäffer Gregianini, Thaísa Regina Rocha Lopes, Eduardo Furtado Flores, Rudi Weiblen, Patrícia Chaves Brites, Mônica de Medeiros Silva, João Batista Teixeira da Rocha, Gustavo de Lima Barbosa, Lais Ceschini Machado, Alexandre Freitas da Silva, Marcelo Henrique Santos Paiva, Matheus Filgueira Bezerra, Tulio de Lima Campos, Tiago Gräf, Daniel Angelo Sganzerla Graichen, Elgion Lucio da Silva Loreto, Gabriel da Luz Wallau
Publikováno v:
Virus Research, Vol 351, Iss , Pp 199500- (2025)
Brazil's COVID-19 response has faced challenges due to the continuous emergence of variants of concern (VOCs), emphasizing the need for ongoing genomic surveillance and retrospective analyses of past epidemic waves to reassess and fine tune containme
Externí odkaz:
https://doaj.org/article/aaf844fc6548499992b5d794888741e5
