TY - GEN
T1 - Approximate Quantum Amplitude Encoding with Parameterized Quantum Circuits
AU - Zheng, Jin
AU - Gao, Qing
AU - Ogorzalek, Maciej
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - This paper proposes an approximate quantum amplitude encoding (AQAE) model that can be deployed on parameterized quantum circuits (PQCs), providing a novel avenue to efficiently encode quantum states with shallow circuit depth and a reduced number of quantum gates. The AQAE is established by the simulation quantum circuit and the implementation quantum circuit. Simulation results on public datasets demonstrate the high-fidelity encoding capability, the efficient learning capability, and the robustness of the proposed model.
AB - This paper proposes an approximate quantum amplitude encoding (AQAE) model that can be deployed on parameterized quantum circuits (PQCs), providing a novel avenue to efficiently encode quantum states with shallow circuit depth and a reduced number of quantum gates. The AQAE is established by the simulation quantum circuit and the implementation quantum circuit. Simulation results on public datasets demonstrate the high-fidelity encoding capability, the efficient learning capability, and the robustness of the proposed model.
KW - Parameterized quantum circuits
KW - quantum encoding
KW - quantum neural network
UR - https://www.scopus.com/pages/publications/105019059738
U2 - 10.1109/qCCL65142.2025.11159050
DO - 10.1109/qCCL65142.2025.11159050
M3 - 会议稿件
AN - SCOPUS:105019059738
T3 - Proceedings of 2025 IEEE International Conference on Quantum Control, Computing and Learning, qCCL 2025
SP - 155
EP - 160
BT - Proceedings of 2025 IEEE International Conference on Quantum Control, Computing and Learning, qCCL 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 IEEE International Conference on Quantum Control, Computing and Learning, qCCL 2025
Y2 - 25 June 2025 through 28 June 2025
ER -