Zobrazeno 1 - 10
of 2 184
pro vyhledávání: '"P, Panella"'
Autor:
Ajmal, Sehar, Azzi, Patrizia, Giappichini, Sofia, Klute, Markus, Panella, Orlando, Presilla, Matteo, Zuo, Xunwu
This paper investigates the search for heavy neutral leptons (HNL) in the type I seesaw mechanism at the Future Circular Collider in its $e^+e^-$ stage (FCC-ee), considering an integrated luminosity of 204 ab$^{-1}$ collected at the Z pole. The study
Externí odkaz:
http://arxiv.org/abs/2410.03615
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 construct charged, static black holes in three-dimensional de Sitter (dS$_{3}$) space that exactly account for semi-classical backreaction effects due to quantum conformal matter. This is accomplished using braneworld holography, where an accelera
Externí odkaz:
http://arxiv.org/abs/2410.02375
Generating images from brain waves is gaining increasing attention due to its potential to advance brain-computer interface (BCI) systems by understanding how brain signals encode visual cues. Most of the literature has focused on fMRI-to-Image tasks
Externí odkaz:
http://arxiv.org/abs/2410.02780
Quantum gravity has long remained elusive from an observational standpoint. Developing effective cosmological models motivated by the fundamental aspects of quantum gravity is crucial for bridging theory with observations. One key aspect is the granu
Externí odkaz:
http://arxiv.org/abs/2409.02188
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
In the context of artificial intelligence, the inherent human attribute of engaging in logical reasoning to facilitate decision-making is mirrored by the concept of explainability, which pertains to the ability of a model to provide a clear and inter
Externí odkaz:
http://arxiv.org/abs/2407.14865
Autor:
Verdone, A., Devoto, A., Sebastiani, C., Carmignani, J., D'Onofrio, M., Giagu, S., Scardapane, S., Panella, M.
The experiments at the Large Hadron Collider at CERN generate vast amounts of complex data from high-energy particle collisions. This data presents significant challenges due to its volume and complex reconstruction, necessitating the use of advanced
Externí odkaz:
http://arxiv.org/abs/2407.14859