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quant-ph2022
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…
quant-ph2019
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…
quant-ph2018
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…