TorchQuantum Case Study for Robust Quantum Circuits
Main Authors: | Wang, Hanrui, Liang, Zhiding, Gu, Jiaqi, Li, Zirui, Ding, Yongshan, Jiang, Weiwen, Shi, Yiyu, Pan, David Z., Chong, Frederic T., Han, Song |
---|---|
Other Authors: | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
Format: | Article |
Language: | English |
Published: |
ACM|IEEE/ACM International Conference on Computer-Aided Design
2023
|
Online Access: | https://hdl.handle.net/1721.1/147680 |
Similar Items
-
QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization
by: Wang, Hanrui, et al.
Published: (2022) -
QOC: Quantum On-Chip Training with Parameter Shift and Gradient 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) -
Variational quantum circuit decoupling
by: Wang, Ximing, et al.
Published: (2024) -
Sparse Quantum Codes from Quantum Circuits
by: Bacon, Dave, et al.
Published: (2015)