Quantum Gate Circuit Neural Network Optimization Algorithm Based on Performance Function

Xuan HOU, Shao-song WAN, Rui LIU


The current status of Quantum Neural Network (QNN) research is analyzed, it deeply studies the model Quantum Gate Circuit Neural Network (QGCNN) and QGCNN Learning Algorithm (QGCA). Replacing the mean square error function by using the Widrow function and the Rumelhart function, it creates two quantum neural network learning algorithms based on performance function, including Learning Algorithm of QGCNN based on the Widrow functions (WQGCA) and Learning Algorithm of QGCNN based on the Rumelhart functions (RQGCA). New algorithms overcome the inherent defects of the training result is not ideal and unrealistic, which is because the use of mean square error function training network will appear excessive punishment phenomenon. Three algorithms are trained by three data sets. Simulation Experiments on three kinds of algorithms was carried out by using these data sets in the case of the best learning rate. Research prove WQGCA and RQGCA have better pattern classification ability relative to QGCA and RQGCA has a higher classification accuracy than WQGCA.


Quantum Neural Network, Error Function, Performance Function, Quantum Gate, Pattern Recognition


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