Li Ziyang, Fu Xiaofei, Meng Lingdong, Du Ruishan
School of Earth Sciences, Northeast Petroleum University, Daqing, 163318, Heilongjiang, China.
Sci Rep. 2025 Sep 1;15(1):32111. doi: 10.1038/s41598-025-17651-5.
With the rapid development of quantum machine learning, quantum neural networks (QNNs) have become a research hotspot. However, the quantum gates used to implement feature mapping in this model are all linear transformations, which directly affects the mapping ability of the model. Therefore, how to enhance the mapping capability of QNN is an important issue that has not yet been effectively addressed. This paper proposes a repetitive amplitude encoding method that encodes the probability amplitudes of multiple qubit blocks by repeatedly using the same set of classical data, effectively improving the mapping capability of QNN. Taking the MNIST dataset as an example, the experimental results comparing the repetitive amplitude encoding method with several existing encoding methods show that, firstly, when the number of classes is fixed, the repetitive amplitude encoding is superior to other methods. Secondly, when the number of hidden layers in QNN is fixed, as the number of classes increases, the performance of repetitive amplitude encoding not only consistently outperforms other methods, but this advantage becomes increasingly apparent. Finally, the repetitive amplitude encoding-based QNN was applied to reservoir lithology identification in the field of oil and gas exploration, IRIS and WINe classification datasets. By comparing with classical neural networks, the proposed method was validated for its adaptability to different classification problems and superior classification performance compared to classical neural networks.
随着量子机器学习的快速发展，量子神经网络（QNNs）已成为一个研究热点。然而，该模型中用于实现特征映射的量子门均为线性变换，这直接影响了模型的映射能力。因此，如何提高QNN的映射能力是一个尚未得到有效解决的重要问题。本文提出了一种重复幅度编码方法，通过重复使用同一组经典数据对多个量子比特块的概率幅度进行编码，有效提高了QNN的映射能力。以MNIST数据集为例，将重复幅度编码方法与几种现有编码方法进行比较的实验结果表明，首先，当类别数量固定时，重复幅度编码优于其他方法。其次，当QNN中的隐藏层数量固定时，随着类别数量的增加，重复幅度编码的性能不仅始终优于其他方法，而且这种优势越来越明显。最后，将基于重复幅度编码的QNN应用于油气勘探领域的储层岩性识别、IRIS和WINe分类数据集。通过与经典神经网络进行比较，验证了所提方法对不同分类问题的适应性以及相对于经典神经网络的优越分类性能。
