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Quantum Neural Machine Learning: Backpropagation and Dynamics
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
doi:10.14704/nq.2017.15.1.1008
fatcat:k7aur5gayzgtlkktw3reyodbf4