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

@article{Baek_Yun_Kim_2024, title={FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features (Student Abstract)}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26938}, DOI={10.1609/aaai.v37i13.26938}, abstractNote={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).}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Baek, Hankyul and Yun, Won Joon and Kim, Joongheon}, year={2024}, month={Jul.}, pages={16156-16157} }