14 citations · 33 across the 8 of their papers we have counts for
8 papers
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…