Direct Fidelity Estimation of Quantum States Using Machine Learning,Physical Review Letters

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Direct Fidelity Estimation of Quantum States Using Machine Learning
Physical Review Letters ( IF 8.1 ) Pub Date : 2021-09-24 , DOI: 10.1103/physrevlett.127.130503
Xiaoqian Zhang ₁ , Maolin Luo ₁ , Zhaodi Wen ₂ , Qin Feng ₁ , Shengshi Pang ₁ , Weiqi Luo ₂ , Xiaoqi Zhou ₁

Affiliation

In almost all quantum applications, one of the key steps is to verify that the fidelity of the prepared quantum state meets expectations. In this Letter, we propose a new approach solving this problem using machine-learning techniques. Compared to other fidelity estimation methods, our method is applicable to arbitrary quantum states, the number of required measurement settings is small, and this number does not increase with the size of the system. For example, for a general five-qubit quantum state, only four measurement settings are required to predict its fidelity with

\pm 1 %

precision in a nonadversarial scenario. This machine-learning-based approach for estimating quantum state fidelity has the potential to be widely used in the field of quantum information.

中文翻译：

使用机器学习直接估计量子态的保真度

在几乎所有的量子应用中，关键步骤之一是验证准备好的量子态的保真度是否符合预期。在这封信中，我们提出了一种使用机器学习技术解决这个问题的新方法。与其他保真度估计方法相比，我们的方法适用于任意量子态，所需的测量设置数量很少，而且这个数量不会随着系统规模的增加而增加。例如，对于一般的五量子位量子态，只需要四个测量设置来预测其保真度