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Quantity study on a novel quantum neural network with alternately controlled gates for binary image classification

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Abstract

A novel quantum neural network(QNN) is proposed, in which quantum probability image encoding(QPIE) and specially designed ansatz are used. QPIE can exponentially reduce qubits for image encoding by using quantum superposition. The parameter gates in ansatz are selected from the universal gate set for quantum computing, which guarantees the expressibility of models. The proposed QNN can be trained by supervised learning. In this article, various experiments are conducted to explore the factors that affect accuracy. The results derive from MNIST show that both the improvement of resolution and the repetition of layers have a positive contribution to accuracy. The enhancement of the expressibility of a single layer by replacing CX gates with \(\hbox {R}_y\) gates also improves the performance of the model.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by the fundamental research funds for the central universities [Project No.K20210337]

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Authors and Affiliations

  1. School of Mathematical Sciences, Zhejiang University, No. 38, Zheda Road, Hangzhou, 310027, Zhejiang, People’s Republic of China

    Qi Bai & Xianliang Hu

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  1. Qi Bai
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  2. Xianliang Hu
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Correspondence to Xianliang Hu.

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Bai, Q., Hu, X. Quantity study on a novel quantum neural network with alternately controlled gates for binary image classification. Quantum Inf Process 22, 184 (2023). https://doi.org/10.1007/s11128-023-03929-y

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