Variational quantum algorithms are a leading candidate for early applications on noisy intermediate-scale quantum computers. These algorithms depend on a classical optimization outer-loop that minimizes some function of a parameterized quantum circuit. In practice, finite sampling error and gate errors make this a stochastic optimization with unique challenges that must be addressed at the level of the optimizer. The sharp trade-off between precision and sampling time in conjunction with experimental constraints necessitates the development of new optimization strategies to minimize overall wall clock time in this setting. In this work, we introduce two optimization methods and numerically compare their performance with common methods in use today. The methods are surrogate model-based algorithms designed to improve reuse of collected data. They do so by utilizing a least-squares quadratic fit of sampled function values within a moving trusted region to estimate the gradient or ...

中文翻译：

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使用模型改进变分量子算法的优化器

变分量子算法是在嘈杂的中型量子计算机上早期应用的首选。这些算法依赖于经典的优化外环，该外环最小化了参数化量子电路的某些功能。在实践中，有限的采样误差和门误差使之成为具有唯一挑战的随机优化，必须在优化器一级解决该挑战。精确度和采样时间之间的权衡取舍，再加上实验性约束，因此需要开发新的优化策略，以在这种情况下最大程度地缩短整体挂钟时间。在这项工作中，我们介绍了两种优化方法，并将它们的性能与当今使用的常见方法进行了数值比较。这些方法是基于代理模型的算法，旨在提高收集数据的重用性。