TY - GEN
T1 - Design of a Quantum Self-Attention Neural Network on Quantum Circuits
AU - Zheng, Jin
AU - Gao, Qing
AU - Miao, Zibo
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This paper proposes a quantum self-attention neural network (QSAN) model that can be deployed on quantum circuits, providing a novel avenue to processing text classification tasks in natural language processing (NLP). The QSAN framework is established by integrating four basic blocks: the data preprocessing block, the quantum encoding block, the model design block, and the network optimization block. Simulation results demonstrate remarkable convergence and accuracy on various text classification datasets. In particular, the proposed QSAN surpasses the existing state-of-the-art quantum NLP (QNLP) model in terms of test accuracy.
AB - This paper proposes a quantum self-attention neural network (QSAN) model that can be deployed on quantum circuits, providing a novel avenue to processing text classification tasks in natural language processing (NLP). The QSAN framework is established by integrating four basic blocks: the data preprocessing block, the quantum encoding block, the model design block, and the network optimization block. Simulation results demonstrate remarkable convergence and accuracy on various text classification datasets. In particular, the proposed QSAN surpasses the existing state-of-the-art quantum NLP (QNLP) model in terms of test accuracy.
UR - https://www.scopus.com/pages/publications/85187243650
U2 - 10.1109/SMC53992.2023.10393989
DO - 10.1109/SMC53992.2023.10393989
M3 - 会议稿件
AN - SCOPUS:85187243650
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 1058
EP - 1063
BT - 2023 IEEE International Conference on Systems, Man, and Cybernetics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023
Y2 - 1 October 2023 through 4 October 2023
ER -