Backpropagation training in adaptive quantum networks

Christopher Altman; Romàn R. Zapatrin

doi:10.1007/s10773-009-0103-1

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arXiv · 0903.4416

Backpropagation training in adaptive quantum networks

Christopher Altman·Romàn R. Zapatrin

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Abstract

We introduce a robust, error-tolerant adaptive training algorithm for generalized learning paradigms in high-dimensional superposed quantum networks, or \emph{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.

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Christopher Altman, Romàn R. Zapatrin. 2009-03-25. Backpropagation training in adaptive quantum networks. https://doi.org/10.1007/s10773-009-0103-1

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