QOC: Quantum On-Chip Training with Parameter Shift and Gradient Pruning
Main Authors: | Wang, Hanrui, Li, Zirui, Gu, Jiaqi, Ding, Yongshan, Pan, David Z., Han, Song |
---|---|
Other Authors: | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
Format: | Article |
Language: | English |
Published: |
ACM|Proceedings of the 59th ACM/IEEE Design Automation Conference
2022
|
Online Access: | https://hdl.handle.net/1721.1/146411 |
Similar Items
-
QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization
by: Wang, Hanrui, et al.
Published: (2022) -
TorchQuantum Case Study for Robust Quantum Circuits
by: Wang, Hanrui, et al.
Published: (2023) -
SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning
by: Wang, Hanrui, et al.
Published: (2022) -
RobustAnalog: Fast Variation-Aware Analog Circuit Design Via Multi-task RL
by: Shi, Wei, et al.
Published: (2022) -
APQ: Joint Search for Network Architecture, Pruning and Quantization Policy
by: Wang, Tianzhe, et al.
Published: (2021)