Quantum artificial neural networks: architectures and components

Narayanan, Ajit and Menneer, Tammy (2000) Quantum artificial neural networks: architectures and components. Information Sciences, 128 (3-4), 231-255. (doi:10.1016/S0020-0255(00)00055-4).

Record type: Article

Abstract

It is shown by classical simulation and experimentation that quantum artificial neural networks (QUANNs) are more efficient and in some cases more powerful than classical artificial neural networks (CLANNs) for a variety of experimental tasks. This effect is particularly noticeable with larger and more complex domains. The gain in efficiency is achieved with no generalisation loss in most cases. QUANNs are also more powerful than CLANNs, again for some of the tasks examined, in terms of what the network can learn. What is more, it appears that not all components of a QUANN architecture need to be quantum for these advantages to surface. It is demonstrated that a fully quantum neural network has no advantage over a partly quantum network and may in fact produce worse results. Overall, this work provides a first insight into the expected behaviour of individual components of QUANNs, if and when quantum hardware is ever built, and raises questions about the interface between quantum and classical components of future QUANNs.

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