38 citations · 85 across the 8 of their papers we have counts for
9 papers
Consensus ranking by quantum annealing
Daniele Franch, Enrico Zardini, Enrico Blanzieri +1
Consensus ranking is a technique used to derive a single ranking that best represents the preferences of multiple individuals or systems. It aims to aggregate different rankings in…
Local Binary and Multiclass SVMs Trained on a Quantum Annealer
Enrico Zardini, Amer Delilbasic, Enrico Blanzieri +2
Support vector machines (SVMs) are widely used machine learning models (e.g., in remote sensing), with formulations for both classification and regression tasks. In the last years,…
Ensembles of Quantum Classifiers
Emiliano Tolotti, Enrico Zardini, Enrico Blanzieri +1
In the current era, known as Noisy Intermediate-Scale Quantum (NISQ), encoding large amounts of data in the quantum devices is challenging and the impact of noise significantly aff…
A general learning scheme for classical and quantum Ising machines
Ludwig Schmid, Enrico Zardini, Davide Pastorello
An Ising machine is any hardware specifically designed for finding the ground state of the Ising model. Relevant examples are coherent Ising machines and quantum annealers. In this…
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…
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…
Seeking Quality Diversity in Evolutionary Co-design of Morphology and Control of Soft Tensegrity Modular Robots
Enrico Zardini, Davide Zappetti, Davide Zambrano +2
Designing optimal soft modular robots is difficult, due to non-trivial interactions between morphology and controller. Evolutionary algorithms (EAs), combined with physical simulat…