We propose the quaternionic quantum neural network (QQNN) for pattern recognition based on the formulation of quaternionic qubits and the construction of activation operators. In this model, the inputs and targets are represented by quaternionic qubits. The proposed neural network is evaluated through a series of experiments using different benchmark datasets, where the results show its superiority as a classifier in terms of accuracy when it is compared to conventional (real-valued) neural networks.

中文翻译：

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用于分类的四元数量子神经网络

我们提出了基于四元量子位的公式和激活算子的构造的用于模式识别的四元量子神经网络（QQNN）。在此模型中，输入和目标由四元量子位表示。所提出的神经网络通过使用不同基准数据集的一系列实验进行评估，结果表明与传统（实值）神经网络相比，其作为分类器在准确性方面的优越性。