20 citations · 58 across the 9 of their papers we have counts for
5 papers · 1 filter
Q-SCALE: Quantum computing-based Sensor Calibration for Advanced Learning and Efficiency
Lorenzo Bergadano, Andrea Ceschini, Pietro Chiavassa +4
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…
On the Effects of Small Graph Perturbations in the MaxCut Problem by QAOA
Leonardo Lavagna, Simone Piperno, Andrea Ceschini +1
We investigate the Maximum Cut (MaxCut) problem on different graph classes with the Quantum Approximate Optimization Algorithm (QAOA) using symmetries. In particular, heuristics on…
From Graphs to Qubits: A Critical Review of Quantum Graph Neural Networks
Andrea Ceschini, Francesco Mauro, Francesca De Falco +7
Quantum Graph Neural Networks (QGNNs) represent a novel fusion of quantum computing and Graph Neural Networks (GNNs), aimed at overcoming the computational and scalability challeng…
A Study on Quantum Graph Neural Networks Applied to Molecular Physics
Simone Piperno, Andrea Ceschini, Su Yeon Chang +3
This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approa…
Towards Efficient Quantum Hybrid Diffusion Models
Francesca De Falco, Andrea Ceschini, Alessandro Sebastianelli +2
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…