International Journal of Theoretical Physics

, Volume 49, Issue 12, pp 2991–2997 | Cite as

Backpropagation Training in Adaptive Quantum Networks

  • Christopher Altman
  • Romàn R. Zapatrin


We introduce a robust, error-tolerant adaptive training algorithm for generalized learning paradigms in high-dimensional superposed quantum networks, or adaptive quantum networks. The formalized procedure applies standard backpropagation training across a coherent ensemble of discrete topological configurations of individual neural networks, each of which is formally merged into appropriate linear superposition within a predefined, decoherence-free subspace. Quantum parallelism facilitates simultaneous training and revision of the system within this coherent state space, resulting in accelerated convergence to a stable network attractor under consequent iteration of the implemented backpropagation algorithm. Parallel evolution of linear superposed networks incorporating backpropagation training provides quantitative, numerical indications for optimization of both single-neuron activation functions and optimal reconfiguration of whole-network quantum structure.


Neural networks Quantum topology Adaptive learning 


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Applied Physics, Kavli Institute of NanoscienceDelft University of TechnologyDelftThe Netherlands
  2. 2.Dept. of InformaticsThe State Russian MuseumSt. PetersburgRussia

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