Quantum Machine Learning (QML) has come into the limelight due to the
exceptional computational abilities of quantum computers. With the promises of
near error-free quantum computers in the not-so-distant future, it is important
that the effect of multi-qubit interactions on quantum neural networks is
studied extensively. This paper introduces a Quantum Convolutional Network with
novel Interaction layers exploiting three-qubit interactions increasing the
network's expressibility and entangling capability, for classifying both image
and one-dimensional data. The proposed approach is tested on three publicly
available datasets namely MNIST, Fashion MNIST, and Iris datasets, to perform
binary and multiclass classifications and is found to supersede the performance
of the existing state-of-the-art methods.

该研究介绍了一种使用三量子比特相互作用增加网络表达能力和纠缠能力的量子卷积网络，通过对 MNIST、Fashion MNIST 和 Iris 数据集进行二分类和多分类，发现其性能超越了现有的最先进方法。