Machine learning approach for quantum non-Markovian noise classification

In this paper, machine learning and artificial neural network models are proposed for quantum noise classification in stochastic quantum dynamics. For this purpose, we train and then validate support vector machine, multi-layer perceptron and recurrent neural network, models with different complexity and accuracy, to solve supervised binary classification problems... (read more)

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Methods used in the Paper


METHOD TYPE
BiLSTM
Bidirectional Recurrent Neural Networks
BiGRU
Bidirectional Recurrent Neural Networks
GRU
Recurrent Neural Networks
Tanh Activation
Activation Functions
Sigmoid Activation
Activation Functions
LSTM
Recurrent Neural Networks
SVM
Non-Parametric Classification