A quantum convolutional neural network on NISQ devices

AAPPS Bulletin

ZengRong Zhou¹  ShiJie Wei¹  GuiLu Long¹  YanHu Chen² 

[1]Beijing Academy of Quantum Information Sciences

[2]Institute of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications

关键词: Quantum computing;    Quantum algorithm;    Quantum machine learning;   

DOI  :  10.1007/s43673-021-00030-3

来源: DOAJ

【 摘 要 】

Abstract Quantum machine learning is one of the most promising applications of quantum computing in the noisy intermediate-scale quantum (NISQ) era. 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 is 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.

【 授权许可】

Unknown