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.

翻译：量子机器学习（QML）因量子计算机卓越的计算能力而备受关注。随着近乎无误差量子计算机在不久将来的实现，深入研究多量子比特交互对量子神经网络的影响至关重要。本文提出一种包含新型交互层的量子卷积网络，通过利用三量子比特交互增强网络的表达能力和纠缠能力，用于图像与一维数据的分类。该方法在MNIST、Fashion MNIST及Iris三个公开数据集上进行二分类与多分类测试，其性能超越了现有最优方法。