TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.14.5.132
TI  - Parameter-parallel distributed variational quantum algorithm
PY  - 2023/05/26
UR  - https://www.scipost.org/SciPostPhys.14.5.132
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 14
IS  - 5
SP  - 132
A1  - Niu, Yun-Fei
AU  - Zhang, Shuo
AU  - Ding, Chen
AU  - Bao, Wan-Su
AU  - Huang, He-Liang
AB  - Variational quantum algorithms (VQAs) have emerged as a promising near-term technique to explore practical quantum advantage on noisy intermediate-scale quantum (NISQ) devices. However, the inefficient parameter training process due to the incompatibility with backpropagation and the cost of a large number of measurements, posing a great challenge to the large-scale development of VQAs. Here, we propose a parameter-parallel distributed variational quantum algorithm (PPD-VQA), to accelerate the training process by parameter-parallel training with multiple quantum processors. To maintain the high performance of PPD-VQA in the realistic noise scenarios, a alternate training strategy is proposed to alleviate the acceleration attenuation caused by noise differences among multiple quantum processors, which is an unavoidable common problem of distributed VQA. Besides, the gradient compression is also employed to overcome the potential communication bottlenecks. The achieved results suggest that the PPD-VQA could provide a practical solution for coordinating multiple quantum processors to handle large-scale real-word applications.
ER  -

@Article{10.21468/SciPostPhys.14.5.132,
	title={{Parameter-parallel distributed variational quantum algorithm}},
	author={Yun-Fei Niu and Shuo Zhang and Chen Ding and Wan-Su Bao and He-Liang Huang},
	journal={SciPost Phys.},
	volume={14},
	pages={132},
	year={2023},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.14.5.132},
	url={https://scipost.org/10.21468/SciPostPhys.14.5.132},
}