11 citations · 53 across the 8 of their papers we have counts for
9 papers
Quantum Convolutional Neural Networks for the detection of Gamma-Ray Bursts in the AGILE space mission data
A. Rizzo, N. Parmiggiani, A. Bulgarelli +17
Quantum computing represents a cutting-edge frontier in artificial intelligence. It makes use of hybrid quantum-classical computation which tries to leverage quantum mechanic princ…
MAQA: A Quantum Framework for Supervised Learning
Antonio Macaluso, Matthias Klusch, Stefano Lodi +1
Quantum Machine Learning has the potential to improve traditional machine learning methods and overcome some of the main limitations imposed by the classical computing paradigm. Ho…
Quantum Splines for Non-Linear Approximations
Antonio Macaluso, Luca Clissa, Stefano Lodi +1
Quantum Computing offers a new paradigm for efficient computing and many AI applications could benefit from its potential boost in performance. However, the main limitation is the…
Enabling Non-Linear Quantum Operations through Variational Quantum Splines
Matteo Antonio Inajetovic, Filippo Orazi, Antonio Macaluso +2
The postulates of quantum mechanics impose only unitary transformations on quantum states, which is a severe limitation for quantum machine learning algorithms. Quantum Splines (QS…
Self-Supervised Bernoulli Autoencoders for Semi-Supervised Hashing
Ricardo Ñanculef, Francisco Mena, Antonio Macaluso +2
Semantic hashing is an emerging technique for large-scale similarity search based on representing high-dimensional data using similarity-preserving binary codes used for efficient…
Quantum Ensemble for Classification
Antonio Macaluso, Luca Clissa, Stefano Lodi +1
A powerful way to improve performance in machine learning is to construct an ensemble that combines the predictions of multiple models. Ensemble methods are often much more accurat…
Fast and Scalable Lasso via Stochastic Frank-Wolfe Methods with a Convergence Guarantee
Emanuele Frandi, Ricardo Nanculef, Stefano Lodi +2
Frank-Wolfe (FW) algorithms have been often proposed over the last few years as efficient solvers for a variety of optimization problems arising in the field of Machine Learning. T…
A Novel Frank-Wolfe Algorithm. Analysis and Applications to Large-Scale SVM Training
Hector Allende, Emanuele Frandi, Ricardo Nanculef +1
Recently, there has been a renewed interest in the machine learning community for variants of a sparse greedy approximation procedure for concave optimization known as {the Frank-W…
Training Support Vector Machines Using Frank-Wolfe Optimization Methods
Emanuele Frandi, Ricardo Nanculef, Maria Grazia Gasparo +2
Training a Support Vector Machine (SVM) requires the solution of a quadratic programming problem (QP) whose computational complexity becomes prohibitively expensive for large scale…