Abstract
Quantum processors promise a paradigm shift in high-performance computing which needs to be assessed by accurate benchmarking measures. In this work, we introduce a new benchmark for variational quantum algorithm (VQA), recently proposed as a heuristic algorithm for small-scale quantum processors. In VQA, a classical optimization algorithm guides the quantum dynamics of the processor to yield the best solution for a given problem. A complete assessment of scalability and competitiveness of VQA should take into account both the quality and the time of dynamics optimization. The method of optimal stopping, employed here, provides such an assessment by explicitly including time as a cost factor. Here we showcase this measure for benchmarking VQA as a solver for some quadratic unconstrained binary optimization. Moreover we show that a better choice for the cost function of the classical routine can significantly improve the performance of the VQA algorithm and even improving it's scaling properties.
Abstract (translated)
量子处理器承诺高性能计算需要通过准确的基准测量来评估模式转变。在这项工作中，我们为变分量子算法（VQA）引入了一个新的基准，最近被提出作为小规模量子处理器的启发式算法。在VQA中，经典的优化算法引导处理器的量子动力学，为给定问题提供最佳解决方案。对VQA的可扩展性和竞争力的全面评估应考虑动态优化的质量和时间。这里采用的最佳停车方法通过将时间明确地包括在成本因素中来提供这样的评估。在这里，我们展示了这种测量方法，用于将VQA作为一些二次无约束二元优化的求解器。此外，我们表明，对于经典例程的成本函数更好的选择可以显着提高VQA算法的性能，甚至改善它的缩放性能。
URL
https://arxiv.org/abs/1710.05365
