Quantum learning for neural associative memories

Quantum information processing in neural structures results in an exponential increase of patterns storage capacity and can explain the extensive memorization and inferencing capabilities of humans. An example can be found in neural associative memories if the synaptic weights are taken to be fuzzy...

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Bibliographic Details
Published in:Fuzzy sets and systems 2006-07, Vol.157 (13), p.1797-1813
Main Authors: Rigatos, G.G., Tzafestas, S.G.
Format: Article
Language:English
Subjects:
Applied sciences
Associative memories
Circuit properties
Computer science
control theory
systems
Control theory. Systems
Digital circuits
Eigenstructure analysis
Electric, optical and optoelectronic circuits
Electronic circuits
Electronics
Exact sciences and technology
Hebbian learning
Information, signal and communications theory
Mathematical methods
Miscellaneous
Quantum learning
Strong fuzzy partition
System theory
Telecommunications and information theory
Theoretical computing
Unitary rotations
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Summary:Quantum information processing in neural structures results in an exponential increase of patterns storage capacity and can explain the extensive memorization and inferencing capabilities of humans. An example can be found in neural associative memories if the synaptic weights are taken to be fuzzy variables. In that case, the weights’ update is carried out with the use of a fuzzy learning algorithm which satisfies basic postulates of quantum mechanics. The resulting weight matrix can be decomposed into a superposition of associative memories. Thus, the fundamental memory patterns (attractors) can be mapped into different vector spaces which are related to each other via unitary rotations. Quantum learning increases the storage capacity of associative memories by a factor of 2 N , where N is the number of neurons.
ISSN:0165-0114
1872-6801
DOI:10.1016/j.fss.2006.02.012