International Journal of Theoretical Physics

, Volume 43, Issue 12, pp 2435–2445 | Cite as

Superpositional Quantum Network Topologies

  • Christopher Altman
  • Jaroslaw Pykacz
  • Romàn R. Zapatrin


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.

Neural networks quantum topology 


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Copyright information

© Springer Science+Business Media, Inc. 2004

Authors and Affiliations

  • Christopher Altman
    • 1
    • 2
  • Jaroslaw Pykacz
    • 3
    • 4
  • Romàn R. Zapatrin
    • 5
  1. 1.Quantum Information Science and Technology ProjectATIPTokyoJapan
  2. 2.Universiteit van AmsterdamThe Netherlands
  3. 3.Instytut MatematykiUniwersytet GdańskiWita StwoszaPoland
  4. 4.Center Leo Apostel of the Vrije Universiteit Brussels (VUB)Brussel
  5. 5.Friedmann Lab. for Theoretical PhysicsSPb UEFGriboyedovaRussia

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