Parameter-parallel distributed variational quantum algorithm

Abstract

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.

Warm Starting Variational Quantum Algorithms with Near Clifford Circuits

“…Because the estimation of the expectation values requires repeatedly running and taking measurements of a PQC with the same initial state, the number of the measurements and the number enormously increase with the number of the qubits and trainable parameters. To address the issue of accelerating the training process of a VQA, a variety of improved approaches have been proposed, such as distributed VQA schemes by simultaneously training with multiple quantum processors [22,23], higher-efficiency estimation methods with fewer measurement shots [24][25][26], and parameter initialization by a pretraining process [27][28][29].…”