Quantum Convolutional Neural Network for Resource-Efficient Image Classification: A Quantum Random Access Memory (QRAM) Approach

Seunghyeok Oh, Jaeho Choi, Jong Kook Kim, Joongheon Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Convolutional Neural Network (CNN) is a breakthrough learning model that shows outstanding performance in computer vision and deep learning applications. However, it is a relatively burdened model in terms of learning speed and resource usage compared to other learning models when the learning scale becomes large. Quantum Convolutional Neural Network (QCNN) is a novel model as a potential solution using quantum computers to handle this problem. Quantum computers with a limited number of usable qubits needs a resource-efficient method to process large-scale data at once. In addition, Quantum Random Access Memory (QRAM) can store the large data to qubits logarithmically using superposition and entanglement. The QRAM algorithm can design a new QCNN model that can efficiently process in massive data. This paper proposes a more resource and depth efficient model for larger-sized input data and the number of output channels using the QRAM algorithm and efficiently extracting features.

Original languageEnglish
Title of host publication35th International Conference on Information Networking, ICOIN 2021
PublisherIEEE Computer Society
Pages50-52
Number of pages3
ISBN (Electronic)9781728191003
DOIs
Publication statusPublished - 2021 Jan 13
Event35th International Conference on Information Networking, ICOIN 2021 - Jeju Island, Korea, Republic of
Duration: 2021 Jan 132021 Jan 16

Publication series

NameInternational Conference on Information Networking
Volume2021-January
ISSN (Print)1976-7684

Conference

Conference35th International Conference on Information Networking, ICOIN 2021
Country/TerritoryKorea, Republic of
CityJeju Island
Period21/1/1321/1/16

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

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