394 citations · 477 across the 13 of their papers we have counts for
13 papers
Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learning
Kuo-Chung Peng, Samuel Yen-Chi Chen, Jiun-Cheng Jiang +16
Fast Weight Programmers (FWPs) encode temporal dependencies through dynamically updated parameters rather than recurrent hidden states. Quantum FWPs (QFWPs) extend this idea with v…
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…
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…
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…
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…
A Review on Quantum Approximate Optimization Algorithm and its Variants
Kostas Blekos, Dean Brand, Andrea Ceschini +4
The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising variational quantum algorithm that aims to solve combinatorial optimization problems that are classicall…
Resource Saving via Ensemble Techniques for Quantum Neural Networks
Massimiliano Incudini, Michele Grossi, Andrea Ceschini +4
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 limi…
Quasi-Chaotic Oscillators Based on Modular Quantum Circuits
Andrea Ceschini, Antonello Rosato, Massimo Panella
Digital circuits based on residue number systems have been considered to produce a pseudo-random behavior. The present work is an initial step towards the complete implementation o…