NDLI: A Weightless Neural Node Based on a Probabilistic Quantum Memory

Content Provider	IEEE Xplore Digital Library
Author	Silva, A. de Oliveira, W. Ludermir, T.
Copyright Year	2010
Abstract	The success of quantum computation is most commonly associated with speed up of classical algorithms, as the Shor's factoring algorithm and the Grover's search algorithm. But it should also be related with exponential storage capacity such as the super dense coding. In this work we use a probabilistic quantum memory proposed by Trugen berger, where one can store $\mathbf 2^n$ patterns with only $\mathbf n$ quantum bits (qbits). We define a new model of a quantum weightless neural node with this memory in a similar fashion as to the classical Random Access Memory (RAM) node is used in classical weightless neural networks. Some advantages of the proposed model are that the memory of the node does not grow exponentially with the number of inputs and that the node can generalise.
Starting Page	259
Ending Page	264
File Size	238814
Page Count	6
File Format	PDF
ISBN	9781424483914
ISSN	15224899
DOI	10.1109/SBRN.2010.52
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-23
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Random access memory Quantum computing Artificial neural networks Registers Probabilistic logic Equations Pattern recognition Probabilistic Quantum Memories Neural Computing Quantum Computing RAM Based Neural Networks Weightless Neural Networks
Content Type	Text
Resource Type	Article

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