Quantum Neural Machine Learning: Backpropagation and Dynamics

Carlos Pedro Gonçalves
2016 NeuroQuantology  
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 selfprograming 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. the feedforward direction, during the quantum learning stage, quantum information is, then, propagated backwards so that the network effectively functions as a self-programming quantum computing system, efficiently solving computational problems. eISSN 1303-5150 www.neuroquantology.com 23 implemented on the input neurons, conditionally transforming their state in such a way that a given computational problem is solved.
doi:10.14704/nq.2017.15.1.1008 fatcat:k7aur5gayzgtlkktw3reyodbf4