FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features

Hankyul Baek; Won Joon Yun; Joongheon Kim

doi:10.1609/aaai.v37i13.26938

FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features

Hankyul Baek
, Won Joon Yun
, Joongheon Kim^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

Quantum convolutional neural network (QCNN) has just become as an emerging research topic as we experience the noisy intermediate-scale quantum (NISQ) era and beyond. As convolutional filters in QCNN extract intrinsic feature using quantum-based ansatz, it should use only finite number of qubits to prevent barren plateaus, and it introduces the lack of the feature information. In this paper, we propose a novel QCNN training algorithm to optimize feature extraction while using only a finite number of qubits, which is called fidelity-variation training (FV-Training).

Original language	English
Title of host publication	AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations
Editors	Brian Williams, Yiling Chen, Jennifer Neville
Publisher	AAAI press
Pages	16156-16157
Number of pages	2
ISBN (Electronic)	9781577358800
DOIs	https://doi.org/10.1609/aaai.v37i13.26938
Publication status	Published - 2023 Jun 27
Event	37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, United States Duration: 2023 Feb 7 → 2023 Feb 14

Publication series

Name	Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023
Volume	37

Conference

Conference	37th AAAI Conference on Artificial Intelligence, AAAI 2023
Country/Territory	United States
City	Washington
Period	23/2/7 → 23/2/14

Bibliographical note

Publisher Copyright:
Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

ASJC Scopus subject areas

Artificial Intelligence

Access to Document

10.1609/aaai.v37i13.26938

Cite this

Baek, H., Yun, W. J., & Kim, J. (2023). FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features. In B. Williams, Y. Chen, & J. Neville (Eds.), AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations (pp. 16156-16157). (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023; Vol. 37). AAAI press. https://doi.org/10.1609/aaai.v37i13.26938

FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features. / Baek, Hankyul; Yun, Won Joon; Kim, Joongheon.
AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations. ed. / Brian Williams; Yiling Chen; Jennifer Neville. AAAI press, 2023. p. 16156-16157 (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023; Vol. 37).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Baek, H, Yun, WJ & Kim, J 2023, FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features. in B Williams, Y Chen & J Neville (eds), AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations. Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, vol. 37, AAAI press, pp. 16156-16157, 37th AAAI Conference on Artificial Intelligence, AAAI 2023, Washington, United States, 23/2/7. https://doi.org/10.1609/aaai.v37i13.26938

Baek H, Yun WJ, Kim J. FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features. In Williams B, Chen Y, Neville J, editors, AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations. AAAI press. 2023. p. 16156-16157. (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023). doi: 10.1609/aaai.v37i13.26938

Baek, Hankyul ; Yun, Won Joon ; Kim, Joongheon. / FV-Train : Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features. AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations. editor / Brian Williams ; Yiling Chen ; Jennifer Neville. AAAI press, 2023. pp. 16156-16157 (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023).

@inproceedings{e3462c0ca6084655976fbb6bcb937f7e,

title = "FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features",

abstract = "Quantum convolutional neural network (QCNN) has just become as an emerging research topic as we experience the noisy intermediate-scale quantum (NISQ) era and beyond. As convolutional filters in QCNN extract intrinsic feature using quantum-based ansatz, it should use only finite number of qubits to prevent barren plateaus, and it introduces the lack of the feature information. In this paper, we propose a novel QCNN training algorithm to optimize feature extraction while using only a finite number of qubits, which is called fidelity-variation training (FV-Training).",

author = "Hankyul Baek and Yun, \{Won Joon\} and Joongheon Kim",

note = "Publisher Copyright: Copyright {\textcopyright} 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 37th AAAI Conference on Artificial Intelligence, AAAI 2023 ; Conference date: 07-02-2023 Through 14-02-2023",

year = "2023",

month = jun,

day = "27",

doi = "10.1609/aaai.v37i13.26938",

language = "English",

series = "Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023",

publisher = "AAAI press",

pages = "16156--16157",

editor = "Brian Williams and Yiling Chen and Jennifer Neville",

booktitle = "AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations",

}

TY - GEN

T1 - FV-Train

T2 - 37th AAAI Conference on Artificial Intelligence, AAAI 2023

AU - Baek, Hankyul

AU - Yun, Won Joon

AU - Kim, Joongheon

PY - 2023/6/27

Y1 - 2023/6/27

N2 - Quantum convolutional neural network (QCNN) has just become as an emerging research topic as we experience the noisy intermediate-scale quantum (NISQ) era and beyond. As convolutional filters in QCNN extract intrinsic feature using quantum-based ansatz, it should use only finite number of qubits to prevent barren plateaus, and it introduces the lack of the feature information. In this paper, we propose a novel QCNN training algorithm to optimize feature extraction while using only a finite number of qubits, which is called fidelity-variation training (FV-Training).

AB - Quantum convolutional neural network (QCNN) has just become as an emerging research topic as we experience the noisy intermediate-scale quantum (NISQ) era and beyond. As convolutional filters in QCNN extract intrinsic feature using quantum-based ansatz, it should use only finite number of qubits to prevent barren plateaus, and it introduces the lack of the feature information. In this paper, we propose a novel QCNN training algorithm to optimize feature extraction while using only a finite number of qubits, which is called fidelity-variation training (FV-Training).

UR - https://www.scopus.com/pages/publications/85168238487

U2 - 10.1609/aaai.v37i13.26938

DO - 10.1609/aaai.v37i13.26938

M3 - Conference contribution

AN - SCOPUS:85168238487

T3 - Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023

SP - 16156

EP - 16157

BT - AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations

A2 - Williams, Brian

A2 - Chen, Yiling

A2 - Neville, Jennifer

PB - AAAI press

Y2 - 7 February 2023 through 14 February 2023

ER -

FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this