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Quantum Machine Learning

Quantum Neural Machine Learning - Backpropagation and Dynamics

arXiv
Authors: Carlos Pedro Gonçalves

Year

2016

Paper ID

43422

Status

Preprint

Abstract Read

~2 min

Abstract Words

110

Citations

N/A

Abstract

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 that 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.

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  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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  • 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...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:1609.06935 [2] arXiv https://arxiv.org/abs/1609.06935 [3] Publisher https://arxiv.org/abs/1609.06935

Local Citation Graph (Related-Paper Links)

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