As the rapidly evolving field of machine learning continues to produce incredibly useful tools and models, the potential for quantum computing to provide speed up for machine learning algorithms is becoming increasingly desirable. In particular, quantum circuits in place of classical convolutional filters for image detection-based tasks are being investigated for the ability to exploit quantum advantage. However, these attempts, referred to as quantum convolutional neural networks (QCNNs), lack the ability to efficiently process data with multiple channels and therefore are limited to relatively simple inputs. In this work, we present a variety of hardware-adaptable quantum circuit ansatzes for use as convolutional kernels, and demonstrate that the quantum neural networks we report outperform existing QCNNs on classification tasks involving multi-channel data. We envision that the ability of these implementations to effectively learn inter-channel information will allow quantum machine learning methods to operate with more complex data. This work is available as open source at https://github.com/anthonysmaldone/QCNN-Multi-Channel-Supervised-Learning.

翻译：随着机器学习领域的快速发展不断产生极为有用的工具和模型，量子计算为机器学习算法提供加速的潜力正变得越来越令人期待。特别是，针对基于图像检测的任务，研究人员正在探索用量子电路替代经典卷积滤波器，以期利用量子优势。然而，这些被称为量子卷积神经网络（QCNNs）的尝试缺乏高效处理多通道数据的能力，因此仅限于处理相对简单的输入。在本工作中，我们提出了一系列适用于硬件可调的量子电路拟设，用作卷积核，并证明我们报告的量子神经网络在处理涉及多通道数据的分类任务时优于现有的QCNNs。我们预计，这些实现方案有效学习通道间信息的能力将使量子机器学习方法能够处理更复杂的数据。本工作以开源形式发布于 https://github.com/anthonysmaldone/QCNN-Multi-Channel-Supervised-Learning。