Shuffle-QUDIO: accelerate distributed VQE with trainability enhancement and measurement reduction
This work addresses a synchronization bottleneck in distributed quantum optimization for chemical problems, offering incremental improvements to accelerate VQE on noisy intermediate-scale quantum machines.
The paper tackles the efficiency degradation in distributed variational quantum eigensolver (VQE) due to synchronization by proposing Shuffle-QUDIO, which reduces communication frequency and improves trainability, achieving faster convergence and lower approximation error in ground state energy estimation tasks.
The variational quantum eigensolver (VQE) is a leading strategy that exploits noisy intermediate-scale quantum (NISQ) machines to tackle chemical problems outperforming classical approaches. To gain such computational advantages on large-scale problems, a feasible solution is the QUantum DIstributed Optimization (QUDIO) scheme, which partitions the original problem into $K$ subproblems and allocates them to $K$ quantum machines followed by the parallel optimization. Despite the provable acceleration ratio, the efficiency of QUDIO may heavily degrade by the synchronization operation. To conquer this issue, here we propose Shuffle-QUDIO to involve shuffle operations into local Hamiltonians during the quantum distributed optimization. Compared with QUDIO, Shuffle-QUDIO significantly reduces the communication frequency among quantum processors and simultaneously achieves better trainability. Particularly, we prove that Shuffle-QUDIO enables a faster convergence rate over QUDIO. Extensive numerical experiments are conducted to verify that Shuffle-QUDIO allows both a wall-clock time speedup and low approximation error in the tasks of estimating the ground state energy of molecule. We empirically demonstrate that our proposal can be seamlessly integrated with other acceleration techniques, such as operator grouping, to further improve the efficacy of VQE.