Parameter-Parallel Distributed Variational Quantum Algorithm
This addresses a bottleneck in scaling VQAs for near-term quantum computing, though it appears incremental as it builds on existing distributed methods.
The paper tackles the inefficient parameter training in variational quantum algorithms (VQAs) by proposing a parameter-parallel distributed VQA (PPD-VQA) that accelerates training using multiple quantum processors, with strategies to mitigate noise differences and communication bottlenecks.
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.