Poster: Scalable Quantum Convolutional Neural Networks for Edge Computing

Abstract

The convolutional neural network (CNN) has become a general approach for image processing in machine learning tasks. Quantum CNN (QCNN) is an emerging method to implement CNN using quantum computing. Quantum computing utilizes the properties of quantum mechanics to perform efficient computing. However, current quantum machines do not support large-scale QCNNs due to a lack of qubits. As a consequence, QCNNs are limited in scale and cannot directly process high-dimensional images. These shortcomings result in suboptimal QCNN performance. Meanwhile, building quantum machines with enough qubits is technically difficult and costly. These obstacles motivate us to design a quantum edge computing (QEC) system capable of achieving the scalability of QCNNs. Quantum machines are organized hierarchically in the QEC system. The quantum machines closer to the users collaboratively load and extract quantum features from the high-dimensional input data. Subsequently, the quantum machine in the next layer collects the extracted features and performs further operations to produce the final results. Each quantum machine in the QEC system is equipped with a local small-scale QCNN to capture the data pattern of its input. The local QCNNs could be combined to form a large-scale QCNN capable of learning and processing high-dimensional data, overcoming hardware limitations and improving performance.