A Degressive Quantum Convolutional Neural Network and for Quantum State Classification and Code Recognition
SSRN, ISSN: 1556-5068
2023
- 132Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
With the rapid development of quantum computing, a variety of quantum convolutional neural network (QCNN) are proposed. However, only 1 features of a n-qubits input are transferred to next layer in quantum pooling layer, which results in the accuracy reduction of QCNN. To solve this problem, a novel QCNN model with a degressive circuit in quantum pooling layer is proposed. In order to enhance the ability of extracting global features, we remove the parameters sharing strategy in quantum convolutional layer, and design a quantum convolutional kernel with global eyesight. In addition, to prevent a sharp feature reduction, a degressive parameterized quantum circuit is used to construct the pooling layer. Specifically, in each pooling layer, the Z-basis measurement is only performed on the first qubit to control the rotation gate on other qubits. Compared with the state-ofthe-art model, i.e., HQCNN, the accuracy of our model increased by 0.9%, 1% and 3% respectively in three tasks: quantum state classification, binary code recognition and quaternary code recognition.
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