Superpositional Quantum Network Topologies

Authors: Christopher Altman¹; Jaroslaw Pykacz²; Romàn Zapatrin³

Source: International Journal of Theoretical Physics, Volume 43, Number 12, December 2004 , pp. 2435-2445(11)

Publisher: Springer

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Abstract:

We introduce superposition-based quantum networks composed of (i) the classical perceptron model of multilayered, feedforward neural networks and (ii) the algebraic model of evolving reticular quantum structures as described in quantum gravity. The main feature of this model is moving from particular neural topologies to a quantum metastructure which embodies many differing topological patterns. Using quantum parallelism, training is possible on superpositions of different network topologies. As a result, not only classical transition functions, but also topology becomes a subject of training. The main feature of our model is that particular neural networks, with different topologies, are quantum states. We consider high-dimensionaldissipative quantum structures as candidates for implementation of the model.

Keywords: Neural networks; quantum topology

Document Type: Research article

DOI: http://dx.doi.org/10.1007/s10773-004-7709-0

Affiliations: 1: Quantum Information Science and Technology Project, ATIP, Tokyo, Japan, 2: Instytut Matematyki, Uniwersytet Gda nacute ski, Wita Stwosza, Gda nacute sk, Poland, ski, Wita Stwosza, Gda nacute sk, Poland, "> 3: Friedmann Lab. for Theoretical Physics, SPb UEF, Griboyedova, St. Petersburg, Russia, Email: zapatrin@rusmuseum.ru