Backpropagation training in adaptive quantum networks

Details Backpropagation training in adaptive quantum networks Backpropagation training in adaptive quantum networks

2009-03-25

arxiv.org/abs/0903.4416v1

International Journal of Theoretical Physics, 49, 2991 (2010)

Biology

Quantitative Biology

Neurons and Cognition

Talk presented at "Quantum Structures - 2008", Gdansk, Poland

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Backpropagation training in adaptive quantum networks does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Backpropagation training in adaptive quantum networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Backpropagation training in adaptive quantum networks will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-68918