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

Hankyul Baek, Won Joon Yun, Joongheon Kim

AAAI 2023 pp. 16156-16157

doi:10.1609/AAAI.V37I13.26938 /aaai/2023/baek2023aaai-fv/

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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Text

Baek et al. "FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.26938

Markdown

[Baek et al. "FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/baek2023aaai-fv/) doi:10.1609/AAAI.V37I13.26938

BibTeX

@inproceedings{baek2023aaai-fv,
  title     = {{FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features (Student Abstract)}},
  author    = {Baek, Hankyul and Yun, Won Joon and Kim, Joongheon},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {16156-16157},
  doi       = {10.1609/AAAI.V37I13.26938},
  url       = {https://mlanthology.org/aaai/2023/baek2023aaai-fv/}
}