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dc.contributor.authorBehrman, Elizabeth C.
dc.contributor.authorSteck, James E.
dc.identifier.citationBehrman, Elizabeth C.; Steck, James E. 2017. Programming quantum annealing computers using machine learning. 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp 288-293en_US
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractCommercial quantum annealing (QA) machines are now being built with hundreds of quantum bits (qubits). These are used as analog computers, to solve optimization problems by annealing to an unknown ground state (the solution), given the Hamiltonian for that problem. We propose and develop a new approach, in which we use machine learning to do the inverse problem: to find the Hamiltonian that will produce a given, desired ground state. We demonstrate successful learning to produce a desired fully entangled state for a two-qubit system, then bootstrap to do the same for three, four, five and six qubits; the amount of additional learning necessary decreases. With these new capabilities the computing possibilities for QA arrays are greatly expanded.en_US
dc.relation.ispartofseries2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC);
dc.titleProgramming quantum annealing computers using machine learningen_US
dc.typeConference paperen_US
dc.rights.holder© 2017, IEEEen_US

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