Quantum Walk to Train a Classical Artificial Neural Network
- URL: http://arxiv.org/abs/2109.00128v2
- Date: Tue, 7 Sep 2021 21:25:13 GMT
- Title: Quantum Walk to Train a Classical Artificial Neural Network
- Authors: Luciano S. de Souza, Jonathan H. A. de Carvalho, Tiago A. E. Ferreira
- Abstract summary: This work proposes a procedure that uses a quantum walk in a complete graph to train classical artificial neural networks.
The methodology employed to train the neural network will adjust the synaptic weights of the output layer, not altering the weights of the hidden layer.
In addition to computational gain, another advantage of the proposed procedure is to be possible to know textita priori the number of iterations required to obtain the solutions.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This work proposes a computational procedure that uses a quantum walk in a
complete graph to train classical artificial neural networks. The idea is to
apply the quantum walk to search the weight set values. However, it is
necessary to simulate a quantum machine to execute the quantum walk. In this
way, to minimize the computational cost, the methodology employed to train the
neural network will adjust the synaptic weights of the output layer, not
altering the weights of the hidden layer, inspired in the method of Extreme
Learning Machine. The quantum walk algorithm as a search algorithm is
quadratically faster than its classic analog. The quantum walk variance is
$O(t)$ while the variance of its classic analog is $O(\sqrt{t})$, where $t$ is
the time or iteration. In addition to computational gain, another advantage of
the proposed procedure is to be possible to know \textit{a priori} the number
of iterations required to obtain the solutions, unlike the classical training
algorithms based on gradient descendent.
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