38 citations · 87 across the 11 of their papers we have counts for
11 papers
Towards socially-competent and culturally-adaptive artificial agents Expressive order, interactional disruptions and recovery strategies
Chiara Bassetti, Enrico Blanzieri, Stefano Borgo +1
The development of artificial agents for social interaction pushes to enrich robots with social skills and knowledge about (local) social norms. One possibility is to distinguish t…
Scalable quantum neural networks by few quantum resources
Davide Pastorello, Enrico Blanzieri
This paper focuses on the construction of a general parametric model that can be implemented executing multiple swap tests over few qubits and applying a suitable measurement proto…
A quantum k-nearest neighbors algorithm based on the Euclidean distance estimation
Enrico Zardini, Enrico Blanzieri, Davide Pastorello
The k-nearest neighbors (k-NN) is a basic machine learning (ML) algorithm, and several quantum versions of it, employing different distance metrics, have been presented in the last…
Quantum Annealing Learning Search Implementations
Andrea Bonomi, Thomas De Min, Enrico Zardini +3
This paper presents the details and testing of two implementations (in C++ and Python) of the hybrid quantum-classical algorithm Quantum Annealing Learning Search (QALS) on a D-Wav…
Evaluating the Convergence of Tabu Enhanced Hybrid Quantum Optimization
Enrico Blanzieri, Davide Pastorello, Valter Cavecchia +2
In this paper we introduce the Tabu Enhanced Hybrid Quantum Optimization metaheuristic approach useful for optimization problem solving on a quantum hardware. We address the theore…
Implementation and Empirical Evaluation of a Quantum Machine Learning Pipeline for Local Classification
Enrico Zardini, Enrico Blanzieri, Davide Pastorello
In the current era, quantum resources are extremely limited, and this makes difficult the usage of quantum machine learning (QML) models. Concerning the supervised tasks, a viable…
Reconstructing Bayesian Networks on a Quantum Annealer
Enrico Zardini, Massimo Rizzoli, Sebastiano Dissegna +2
Bayesian networks are widely used probabilistic graphical models, whose structure is hard to learn starting from the generated data. O'Gorman et al. have proposed an algorithm to e…
A quantum binary classifier based on cosine similarity
Davide Pastorello, Enrico Blanzieri
We introduce the quantum implementation of a binary classifier based on cosine similarity between data vectors. The proposed quantum algorithm evaluates the classifier on a set of…
Learning adiabatic quantum algorithms for solving optimization problems
Davide Pastorello, Enrico Blanzieri
An adiabatic quantum algorithm is essentially given by three elements: An initial Hamiltonian with known ground state, a problem Hamiltonian whose ground state corresponds to the s…
Quantum Annealing Learning Search for solving QUBO problems
Enrico Blanzieri, Davide Pastorello
In this paper we present a novel strategy to solve optimization problems within a hybrid quantum-classical scheme based on quantum annealing, with a particular focus on QUBO proble…
Very Simple Classifier: a Concept Binary Classifier toInvestigate Features Based on Subsampling and Localility
Luca Masera, Enrico Blanzieri
We propose Very Simple Classifier (VSC) a novel method designed to incorporate the concepts of subsampling and locality in the definition of features to be used as the input of a p…