14 citations · 33 across the 8 of their papers we have counts for
12 papers
Quantum Vehicle Routing Problem: a smart initialization strategy for ill-conditioned optimization landscapes
Emanuele Casciaro, Mario De Simone, Stefano Martina +3
With the expansion of complex transport infrastructure, new paths for linking different destinations have emerged, allowing the definition of multiple routes. The introduction of n…
Quantum anomaly detection in real scarce data
Emanuele Casciaro, Fabio Mascherpa, Alfonso Amendola +1
Anomaly detection on small and unbalanced datasets remains very challenging in machine learning, although this scenario is common in several domains, including healthcare, cybersec…
Divide et Impera quantum neural networks for modular hybrid computing architectures
Emanuele Casciaro, Fabio Mascherpa, Alfonso Amendola +1
Quantum machine learning is rapidly emerging as a promising and potentially more sustainable approach that can complement traditional, energy-hungry HPC, AI, and GPU resources-part…
Quantum Diffusion Models for Medical Image Analysis
Francesco Aldo Venturelli, Stefano Martina, Marco Parigi +3
Quantum Machine Learning is a novel field of research aimed at devising machine learning approaches exploiting principles of quantum mechanics, such as superposition, entanglement…
Hybrid quantum-classical neural network for sentiment analysis
Giacomo Cappiello, Filippo Caruso, Xing Liang +1
Quantum machine learning has recently emerged as a promising paradigm that leverages the expressive power of quantum circuits to address complex learning tasks. In this work, we in…
Quantum Generative Diffusion Model for Real-World Time Series
Jack Waller, Filippo Caruso, Dimitrios Makris +2
Generative models have achieved remarkable success in data synthesis, though recent advances driven by increasing model scale have introduced challenges in computational cost and e…
Physics-inspired Generative AI models via real hardware-based noisy quantum diffusion
Marco Parigi, Stefano Martina, Francesco Aldo Venturelli +1
Quantum Diffusion Models (QDMs) are an emerging paradigm in Generative AI that aims to use quantum properties to improve the performances of their classical counterparts. However,…
Investigating layer-selective transfer learning of QAOA parameters for Max-Cut problem
Francesco Aldo Venturelli, Sreetama Das, Filippo Caruso
The quantum approximate optimization algorithm (QAOA) is a variational quantum algorithm (VQA) ideal for noisy intermediate-scale quantum (NISQ) processors, and is highly successfu…
Machine-learning based high-bandwidth magnetic sensing
Galya Haim, Stefano Martina, John Howell +2
Recent years have seen significant growth of quantum technologies, and specifically quantum sensing, both in terms of the capabilities of advanced platforms and their applications.…
Permutation-equivariant quantum convolutional neural networks
Sreetama Das, Filippo Caruso
The Symmetric group manifests itself in large classes of quantum systems as the invariance of certain characteristics of a quantum state with respect to permuting the qubit…
Transfer learning with generative models for object detection on limited datasets
Matteo Paiano, Stefano Martina, Carlotta Giannelli +1
The availability of data is limited in some fields, especially for object detection tasks, where it is necessary to have correctly labeled bounding boxes around each object. A nota…
The role of data embedding in equivariant quantum convolutional neural networks
Sreetama Das, Stefano Martina, Filippo Caruso
Geometric deep learning refers to the scenario in which the symmetries of a dataset are used to constrain the parameter space of a neural network and thus, improve their trainabili…