A quantum information retrieval approach to memory

Kitto, K; Bruza, P; Gabora, L

A quantum information retrieval approach to memory

Kitto, K

Bruza, P Gabora, L

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2012
Issue Date:: 2012-08-22

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Field	Value	Language
dc.contributor.author	Kitto, K https://orcid.org/0000-0001-7642-7121	en_US
dc.contributor.author	Bruza, P	en_US
dc.contributor.author	Gabora, L	en_US
dc.date.issued	2012-08-22	en_US
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2012	en_US
dc.identifier.isbn	9781467314909	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121238
dc.description.abstract	As computers approach the physical limits of information storable in memory, new methods will be needed to further improve information storage and retrieval. We propose a quantum inspired vector based approach, which offers a contextually dependent mapping from the subsymbolic to the symbolic representations of information. If implemented computationally, this approach would provide exceptionally high density of information storage, without the traditionally required physical increase in storage capacity. The approach is inspired by the structure of human memory and incorporates elements of Grdenfors' Conceptual Space approach and Humphreys et al.'s matrix model of memory. © 2012 IEEE.	en_US
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks	en_US
dc.relation.isbasedon	10.1109/IJCNN.2012.6252492	en_US
dc.title	A quantum information retrieval approach to memory	en_US
dc.type	Conference Proceeding
utslib.for	170201 Computer Perception, Memory and Attention	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)/Connected Intelligence Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

As computers approach the physical limits of information storable in memory, new methods will be needed to further improve information storage and retrieval. We propose a quantum inspired vector based approach, which offers a contextually dependent mapping from the subsymbolic to the symbolic representations of information. If implemented computationally, this approach would provide exceptionally high density of information storage, without the traditionally required physical increase in storage capacity. The approach is inspired by the structure of human memory and incorporates elements of Grdenfors' Conceptual Space approach and Humphreys et al.'s matrix model of memory. © 2012 IEEE.

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http://hdl.handle.net/10453/121238