| dc.contributor.author | Behrman, Elizabeth C. | |
| dc.contributor.author | Steck, James E. | |
| dc.date.accessioned | 2014-06-10T04:06:45Z | |
| dc.date.available | 2014-06-10T04:06:45Z | |
| dc.date.issued | 2013-04-17 | |
| dc.identifier.citation | Behrman, 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.2013.6615168 | en_US |
| dc.identifier.isbn | 978-1-4673-6004-3 | |
| dc.identifier.uri | http://dx.doi.org/10.1109/SIS.2013.6615168 | |
| dc.identifier.uri | http://hdl.handle.net/10057/10595 | |
| dc.description | Click on the DOI link to access this conference paper (may not be free). | en_US |
| dc.description.abstract | Complete 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.iso | en_US | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.ispartofseries | Swarm Intelligence (SIS), 2013 IEEE Symposium on; 16-19 Apr. 2013, Singapore | |
| dc.subject | Mathematical model | en_US |
| dc.subject | Neural networks | en_US |
| dc.subject | Quantum computing | en_US |
| dc.subject | Quantum entanglement | en_US |
| dc.subject | Testing | en_US |
| dc.subject | Time measurement | en_US |
| dc.subject | Training | en_US |
| dc.title | A quantum neural network computes its own relative phase | en_US |
| dc.type | Conference paper | en_US |
