20 citations · 58 across the 9 of their papers we have counts for
7 papers · 1 filter
Enriching Earth Observation labeled data with Quantum Conditioned Diffusion Models
Francesco Mauro, Francesca De Falco, Lorenzo Papa +5
The rapid adoption of diffusion models (DMs) in the Earth Observation (EO) domain has unlocked new generative capabilities aimed at producing new samples, whose statistical propert…
Quantum Latent Diffusion Models
Francesca De Falco, Andrea Ceschini, Alessandro Sebastianelli +2
The introduction of quantum concepts is increasingly making its way into generative machine learning models. However, while there are various implementations of quantum Generative…
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…
A General Approach to Dropout in Quantum Neural Networks
Francesco Scala, Andrea Ceschini, Massimo Panella +1
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…