Optimal Quantum Control with Poor Statistics
Optimal Quantum Control with Poor Statistics
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DOI：
10.1103/prxquantum.1.020322
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发表时间：
2019-09
期刊：
影响因子：
9.7
通讯作者：
F. Sauvage;F. Mintert
中科院分区：
文献类型：
--
作者：
F. Sauvage;F. Mintert
学习如何基于实验数据控制量子系统可以帮助我们超越理论建模所施加的限制。由于量子力学固有的概率性质，为了准确地估计可观测值的期望值，对单个量子系统进行多次重复测量从根本上是必要的。因此，需要精确数据的控制算法可能意味着一种实验努力，从而否定了避免理论建模的好处。我们提出了一种基于贝叶斯优化的控制算法，该算法可以在存在较大测量噪声甚至单次测量限制的情况下找到最优控制解。以制备GHZ态为例，通过数值模拟证明了该方法能够以最小的实验努力找到优秀的控制解。
Learning how to control a quantum system based on experimental data can help us to exceed the limitations imposed by theoretical modelling. Due to the intrinsic probabilistic nature of quantum mechanics, it is fundamentally necessary to repeat measurements on individual quantum systems many times in order to estimate the expectation value of an observable with good accuracy. Control algorithms requiring accurate data can thus imply an experimental effort that negates the benefits of avoiding theoretical modelling. We present a control algorithm based on Bayesian optimisation that finds optimal control solutions in the presence of large measurement shot noise and even in the limit of single-shot measurements. With the explicit example of the preparation of a GHZ state, we demonstrate in numerical simulations that this method is capable of finding excellent control solutions in terms of minimal experimental effort.