34 citations · 59 across the 8 of their papers we have counts for
7 papers · 1 filter
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…
Energy fluctuation relations and repeated quantum measurements
Stefano Gherardini, Lorenzo Buffoni, Guido Giachetti +2
In this review paper, we discuss the statistical description in non-equilibrium regimes of energy fluctuations originated by the interaction between a quantum system and a measurem…
New Trends in Quantum Machine Learning
Lorenzo Buffoni, Filippo Caruso
Here we will give a perspective on new possible interplays between Machine Learning and Quantum Physics, including also practical cases and applications. We will explore the ways i…
Experimental Quantum Embedding for Machine Learning
Ilaria Gianani, Ivana Mastroserio, Lorenzo Buffoni +6
The classification of big data usually requires a mapping onto new data clusters which can then be processed by machine learning algorithms by means of more efficient and feasible…
A Path Towards Quantum Advantage in Training Deep Generative Models with Quantum Annealers
Walter Vinci, Lorenzo Buffoni, Hossein Sadeghi +3
The development of quantum-classical hybrid (QCH) algorithms is critical to achieve state-of-the-art computational models. A QCH variational autoencoder (QVAE) was introduced in Re…
Maximal energy extraction via quantum measurement
Andrea Solfanelli, Lorenzo Buffoni, Alessandro Cuccoli +1
We study the maximal amount of energy that can be extracted from a finite quantum system by means of projective measurements. For this quantity we coin the expression "metrotropy"…