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

2022/09/19 by Hankyul Baek, Baek, Hankyul, Won Joon Yun +3
Computer Science · #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Computational Physics and Python Applications

paper · pdf · doi:10.48550/arxiv.2209.08727

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).

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