论文标题
FV-Train：通过提取各种功能的量子卷积神经网络训练和有限数量的Qubit
FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features
论文作者
论文摘要
量子卷积神经网络（QCNN）刚刚成为一个新兴的研究主题，因为我们体验了嘈杂的中间尺度量子（NISQ）时代及以后。作为使用基于量子的ANSATZ的QCNN提取物中固有特征的卷积过滤器，它应仅使用有限数量的Qubits来防止贫瘠的高原，并且引入了缺乏特征信息。在本文中，我们提出了一种新型的QCNN训练算法，以优化特征提取，同时仅使用有限数量的Qubits，这称为Fidelity-Variation-training（FV-Training）。
Quantum convolutional neural network (QCNN) has just become as an emerging research topic as we experience the noisy intermediate-scale quantum (NISQ) era and beyond. As convolutional filters in QCNN extract intrinsic feature using quantum-based ansatz, it should use only finite number of qubits to prevent barren plateaus, and it introduces the lack of the feature information. In this paper, we propose a novel QCNN training algorithm to optimize feature extraction while using only a finite number of qubits, which is called fidelity-variation training (FV-Training).
