36 citations · 179 across the 12 of their papers we have counts for
13 papers
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…
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.…
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…
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…
Deep learning enhanced noise spectroscopy of a spin qubit environment
Stefano Martina, Santiago Hernández-Gómez, Stefano Gherardini +2
The undesired interaction of a quantum system with its environment generally leads to a coherence decay of superposition states in time. A precise knowledge of the spectral content…
Experimental quantum pattern recognition in IBMQ and diamond NVs
Sreetama Das, Jingfu Zhang, Stefano Martina +2
One of the most promising applications of quantum computing is the processing of graphical data like images. Here, we investigate the possibility of realizing a quantum pattern rec…
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…
Learning the noise fingerprint of quantum devices
Stefano Martina, Lorenzo Buffoni, Stefano Gherardini +1
Noise sources unavoidably affect any quantum technological device. Noise's main features are expected to strictly depend on the physical platform on which the quantum device is rea…
Quantum Reinforcement Learning: the Maze problem
Nicola Dalla Pozza, Lorenzo Buffoni, Stefano Martina +1
Quantum Machine Learning (QML) is a young but rapidly growing field where quantum information meets machine learning. Here, we will introduce a new QML model generalizing the class…
Machine learning classification of non-Markovian noise disturbing quantum dynamics
Stefano Martina, Stefano Gherardini, Filippo Caruso
In this paper machine learning and artificial neural network models are proposed for the classification of external noise sources affecting a given quantum dynamics. For this purpo…