A Quantum Convolutional Neural Network on NISQ Devices

TL;DR

This paper introduces a quantum convolutional neural network (QCNN) designed for NISQ devices, offering reduced complexity, robustness to noise, and applicability to image processing and recognition tasks, advancing quantum machine learning.

Contribution

The paper presents a novel QCNN model inspired by classical CNNs, with scalable parameters and noise resilience, suitable for near-term quantum hardware.

Findings

Successfully applied QCNN to image filtering tasks like smoothing, sharpening, and edge detection.

Demonstrated QCNN's effectiveness in handwritten digit recognition.

Showed QCNN's robustness to certain noise conditions.

Abstract

Quantum machine learning is one of the most promising applications of quantum computing in the Noisy Intermediate-Scale Quantum(NISQ) era. Here we propose a quantum convolutional neural network(QCNN) inspired by convolutional neural networks(CNN), which greatly reduces the computing complexity compared with its classical counterparts, with $O((log_{2}M)^6) $ basic gates and $O(m^2+e)$ variational parameters, where $M$ is the input data size, $m$ is the filter mask size and $e$ is the number of parameters in a Hamiltonian. Our model is robust to certain noise for image recognition tasks and the parameters are independent on the input sizes, making it friendly to near-term quantum devices. We demonstrate QCNN with two explicit examples. First, QCNN is applied to image processing and numerical simulation of three types of spatial filtering, image smoothing, sharpening, and edge detection are performed. Secondly, we demonstrate QCNN in recognizing image, namely, the recognition of handwritten numbers. Compared with previous work, this machine learning model can provide implementable quantum circuits that accurately corresponds to a specific classical convolutional kernel. It provides an efficient avenue to transform CNN to QCNN directly and opens up the prospect of exploiting quantum power to process information in the era of big data.