Work

QDNN: DNN with Quantum Neural Network Layers

Year: 2019

Type: preprint

Abstract: In this paper, we introduce a quantum extension of classical DNN, QDNN. The QDNN consisting of quantum structured layers can uniformly approximate any continuous function and has more representation power than the classical DNN. It still keeps the advantages of the classical DNN such as the non-linear activation, the multi-layer structure, and the efficient backpropagation training algorithm. Moreover, the QDNN can be used on near-term noisy intermediate-scale quantum processors. A numerical experiment for image classification based on quantum DNN is given, where a high accuracy rate is achieved.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1912.12660

Authors: Zhao, Chen, Gao, Xiao-Shan

Institutions: Chinese Academy of Sciences, Academy of Mathematics and Systems Science, University of Chinese Academy of Sciences

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Natural Science Foundation of China

Awards: G6967058303, G6976570839

OpenAlex ID: W2997326604

Cites: 38

Cited by: 13

Related to: 10