Quantum Convolutional Neural Networks for Multi-Channel Supervised Learning

TL;DR

This paper introduces new quantum convolutional neural network architectures capable of efficiently processing multi-channel data, outperforming existing models and enabling quantum machine learning on more complex datasets.

Contribution

The authors develop hardware-adaptable quantum circuit ansatzes for convolutional kernels that improve multi-channel data processing in quantum neural networks.

Findings

Quantum neural networks outperform existing QCNNs on multi-channel classification tasks.

New quantum circuit ansatzes effectively learn inter-channel information.

Open source implementation available for further research.

Abstract

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.