论文标题
变异量子算法中随机优化器的延迟注意事项
Latency considerations for stochastic optimizers in variational quantum algorithms
论文作者
论文摘要
在嘈杂的中间量子量子设置中已提高的变异量子算法需要在经典硬件上实现随机优化器。迄今为止，大多数研究都基于随机梯度迭代作为随机经典优化器采用算法。在这项工作中，我们建议使用随机优化算法产生模拟经典确定性算法动力学的随机过程。这种方法导致理论上最差的迭代迭代复杂性的方法，而牺牲了较高的触电样本（SHOT）复杂性。我们在理论上和经验上都研究了这种权衡，并得出结论，选择随机优化器的偏好应明确取决于延迟和射击执行时间的函数。
Variational quantum algorithms, which have risen to prominence in the noisy intermediate-scale quantum setting, require the implementation of a stochastic optimizer on classical hardware. To date, most research has employed algorithms based on the stochastic gradient iteration as the stochastic classical optimizer. In this work we propose instead using stochastic optimization algorithms that yield stochastic processes emulating the dynamics of classical deterministic algorithms. This approach results in methods with theoretically superior worst-case iteration complexities, at the expense of greater per-iteration sample (shot) complexities. We investigate this trade-off both theoretically and empirically and conclude that preferences for a choice of stochastic optimizer should explicitly depend on a function of both latency and shot execution times.
