  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.
