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Quantum Machine Learning

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

arXiv

Authors: Hankyul Baek, Won Joon Yun, Joongheon Kim

Year

2022

Paper ID

59215

Status

Preprint

Abstract Read

~2 min

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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).

Why This Paper Matters

This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2022 reference point for readers tracking recent quantum research.
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.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2209.08727 [2] arXiv https://arxiv.org/abs/2209.08727 [3] Publisher https://arxiv.org/abs/2209.08727

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