论文标题
量子分析下降
Quantum Analytic Descent
论文作者
论文摘要
变化算法与近期量子计算机具有特殊相关性，但需要非平凡的参数优化。在这里，我们提出了分析下降：鉴于能量格局必须在任何参考点附近的局部区域具有某种简单的形式，因此可以通过经典模型有效地将其完整地近似于其 - 我们以严格的，复杂性理论的论证来支持这些观察结果。在确定必要时确定更精致的功能之前，可以直接“跳转”到（估计的）最小值，以直接“跳跃”到（估计的）最小值。我们得出了一种最佳的测量策略，通常证明“跳跃”的渐近资源成本仅对应于单个梯度矢量评估。
Variational algorithms have particular relevance for near-term quantum computers but require non-trivial parameter optimisations. Here we propose Analytic Descent: Given that the energy landscape must have a certain simple form in the local region around any reference point, it can be efficiently approximated in its entirety by a classical model -- we support these observations with rigorous, complexity-theoretic arguments. One can classically analyse this approximate function in order to directly `jump' to the (estimated) minimum, before determining a more refined function if necessary. We derive an optimal measurement strategy and generally prove that the asymptotic resource cost of a `jump' corresponds to only a single gradient vector evaluation.
