8 citations · 11 across the 3 of their papers we have counts for
6 papers · 1 filter
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,…
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.…
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…
Quantum-Noise-Driven Generative Diffusion Models
Marco Parigi, Stefano Martina, Filippo Caruso
Generative models realized with machine learning techniques are powerful tools to infer complex and unknown data distributions from a finite number of training samples in order to…
Machine-learning based noise characterization and correction on neutral atoms NISQ devices
Ettore Canonici, Stefano Martina, Riccardo Mengoni +2
Neutral atoms devices represent a promising technology that uses optical tweezers to geometrically arrange atoms and modulated laser pulses to control the quantum states. A neutral…
Noise fingerprints in quantum computers: Machine learning software tools
Stefano Martina, Stefano Gherardini, Lorenzo Buffoni +1
In this paper we present the high-level functionalities of a quantum-classical machine learning software, whose purpose is to learn the main features (the fingerprint) of quantum n…