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dc.contributor.authorBehrman, Elizabeth C.
dc.contributor.authorSteck, James E.
dc.date.accessioned2014-06-10T04:06:45Z
dc.date.available2014-06-10T04:06:45Z
dc.date.issued2013-04-17
dc.identifier.citationBehrman, E.C.; Steck, J.E., "A quantum neural network computes its own relative phase," Swarm Intelligence (SIS), 2013 IEEE Symposium on , vol., no., pp.119-124, 16-19 April 2013 doi: 10.1109/SIS.2013.6615168en_US
dc.identifier.isbn978-1-4673-6004-3
dc.identifier.urihttp://dx.doi.org/10.1109/SIS.2013.6615168
dc.identifier.urihttp://hdl.handle.net/10057/10595
dc.descriptionClick on the DOI link to access this conference paper (may not be free).en_US
dc.description.abstractComplete characterization of the state of a quantum system made up of subsystems requires determination of relative phase, because of interference effects between the subsystems. For a system of qubits used as a quantum computer this is especially vital, because the entanglement, which is the basis for the quantum advantage in computing, depends intricately on phase. We present here a first step towards that determination, in which we use a two-qubit quantum system as a quantum neural network, which is trained to compute and output its own relative phase.en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.ispartofseriesSwarm Intelligence (SIS), 2013 IEEE Symposium on; 16-19 Apr. 2013, Singapore
dc.subjectMathematical modelen_US
dc.subjectNeural networksen_US
dc.subjectQuantum computingen_US
dc.subjectQuantum entanglementen_US
dc.subjectTestingen_US
dc.subjectTime measurementen_US
dc.subjectTrainingen_US
dc.titleA quantum neural network computes its own relative phaseen_US
dc.typeConference paperen_US


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