38 citations · 87 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…