  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.

Quantum Physics;Artificial Intelligence