Quantum Neural Machine Learning - Backpropagation and Dynamics [article]

Carlos Pedro Gonçalves
2016 arXiv   pre-print
The current work addresses quantum machine learning in the context of Quantum Artificial Neural Networks such that the networks' processing is divided in two stages: the learning stage, where the network converges to a specific quantum circuit, and the backpropagation stage where the network effectively works as a self-programing quantum computing system that selects the quantum circuits to solve computing problems. The results are extended to general architectures including recurrent networks
more » ... hat interact with an environment, coupling with it in the neural links' activation order, and self-organizing in a dynamical regime that intermixes patterns of dynamical stochasticity and persistent quasiperiodic dynamics, making emerge a form of noise resilient dynamical record.
arXiv:1609.06935v1 fatcat:ettnlbe5onfrbgj5cssxlw4rli