| dc.contributor.author | Steck, James E. | |
| dc.contributor.author | Behrman, Elizabeth C. | |
| dc.date.accessioned | 2018-04-25T14:54:39Z | |
| dc.date.available | 2018-04-25T14:54:39Z | |
| dc.date.issued | 2017 | |
| dc.identifier.citation | Steck, James E.; Behrman, Elizabeth C. 2017. Biologically motivated quantum neural networks. 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp 1030-1034 | en_US |
| dc.identifier.isbn | 978-1-5386-1645-1 | |
| dc.identifier.issn | 1062-922X | |
| dc.identifier.other | WOS:000427598701011 | |
| dc.identifier.uri | http://dx.doi.org/10.1109/SMC.2017.8122746 | |
| dc.identifier.uri | http://hdl.handle.net/10057/15006 | |
| dc.description | Click on the DOI link to access the article (may not be free). | en_US |
| dc.description.abstract | This paper presents one step toward creating the building blocks for machine intelligence that is inspired by its biological equivalent. The authors' quantum learning methods (deep quantum learning) are applied to quantum devices whose quantum bit (q-bit) activity is deliberately chosen to mimic the spiking behavior of biological neurons. Because of the "quantum" scale of these computers, these studies may lead to quantum hardware (rather than simulation) with enough processors and enough connectivity that can more closely mimic biological intelligence. | en_US |
| dc.language.iso | en_US | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.ispartofseries | 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC); | |
| dc.subject | Deep | en_US |
| dc.subject | Recognition | en_US |
| dc.subject | Computation | en_US |
| dc.title | Biologically motivated quantum neural networks | en_US |
| dc.type | Conference paper | en_US |
| dc.rights.holder | © 2017, IEEE | en_US |
