7 citations · 8 across the 4 of their papers we have counts for
5 papers
A weighted quantum ensemble of homogeneous quantum classifiers
Emiliano Tolotti, Enrico Blanzieri, Davide Pastorello
Ensemble methods in machine learning aim to improve prediction accuracy by combining multiple models. This is achieved by ensuring diversity among predictors to capture different d…
Enhancing Expressivity of Quantum Neural Networks Based on the SWAP test
Sebastian Nagies, Emiliano Tolotti, Davide Pastorello +1
Quantum neural networks (QNNs) based on parametrized quantum circuits are promising candidates for machine learning applications, yet many architectures lack clear connections to c…
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…