3 papers
cs.NE2025
Quantum Simplicial Neural Networks
Simone Piperno, Claudio Battiloro, Andrea Ceschini +3
Graph Neural Networks (GNNs) excel at learning from graph-structured data but are limited to modeling pairwise interactions, insufficient for capturing higher-order relationships p…
quant-ph2024
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…
quant-ph2024
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…