A Distributed Learning Scheme for Variational Quantum Algorithms

Du, Yuping; Yang, Qian; Wu, Xingyao; Tao, Dacheng

doi:10.1109/tqe.2022.3175267

Cited by 8 publications

(2 citation statements)

References 106 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The PPD-VQA is naturally compatible with the data-parallel distributed VQA proposed in [40], so the combination of the two approaches could enable a stronger acceleration for the training of VQA. When doing such a combination, some methods [48][49][50] can be employed to enhance the generalization ability of data-parallel training [49,50].…”

Section: Discussionmentioning

confidence: 99%

“…To address the above issue, a distributed VQA based on data-parallel has been proposed by Du et. al. to accelerate the training of VQA [40]. In this work, a parameter-parallel distributed variational quantum algorithm (PPD-VQA) is proposed to further accelerate the training process by parameter-parallel training with multiple quantum processors.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Parameter-parallel distributed variational quantum algorithm

et al. 2023

View full text Add to dashboard Cite

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.

show abstract