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 problems. The proposed algorithm implements an iterative structure where the representation of an objective function into the annealer architecture is learned and already visited solutions are penalized by a tabu-inspired search. The result is a heuristic search equipped with a learning mechanism to improve the encoding of the problem into the quantum architecture. We prove the convergence of the algorithm to a global optimum in the case of general QUBO problems. Our technique is an alternative to the direct reduction of a given optimization problem into the sparse annealer graph
Quantum annealing learning search for solving QUBO problems / Pastorello, Davide; Blanzieri, Enrico. - In: QUANTUM INFORMATION PROCESSING. - ISSN 1570-0755. - 2019, 18:10(2019), pp. 303.1-303.17. [10.1007/s11128-019-2418-z]
Quantum annealing learning search for solving QUBO problems
Pastorello, Davide;Blanzieri, Enrico
2019-01-01
Abstract
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 problems. The proposed algorithm implements an iterative structure where the representation of an objective function into the annealer architecture is learned and already visited solutions are penalized by a tabu-inspired search. The result is a heuristic search equipped with a learning mechanism to improve the encoding of the problem into the quantum architecture. We prove the convergence of the algorithm to a global optimum in the case of general QUBO problems. Our technique is an alternative to the direct reduction of a given optimization problem into the sparse annealer graphFile | Dimensione | Formato | |
---|---|---|---|
pastorello2019.pdf
Solo gestori archivio
Descrizione: Articolo
Tipologia:
Versione editoriale (Publisher’s layout)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
325.6 kB
Formato
Adobe PDF
|
325.6 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione