Year: 2022
Type: article
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.
Source: AAPPS bulletin
Authors: ShiJie Wei, YanHu Chen, ZengRong Zhou, GuiLu Long
Institutions: Beijing Academy of Quantum Information Sciences, State Key Laboratory of Low-Dimensional Quantum Physics, Tsinghua University, Beijing University of Posts and Telecommunications, Beijing National Research Center for Information Science and Technology, Frontier Science Center for Quantum Information of the Ministry of Education of China
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,870
Awards: G1654887313, G2038365503, G3329683433, G3504089951, 核磁共振多态相体系的开展研究及其量子纠缠测量, G3794144889, G3851725869, G3886128100, G4301202356, G4321109269, G4336685212, G4344798175, G4518114676, G5252361160, 测量设备无关的量子安全直接通信理论与实验研究, G6725888744, G6837542917, G7399362544, G76247503, G7731819942, G7933946904, G8264566704
OpenAlex ID: W3154952264