论文标题
机器学习辅助量子状态估计
Machine learning assisted quantum state estimation
论文作者
论文摘要
我们建立一个通用的量子状态层析成像框架，该框架利用机器学习技术从给定的一组巧合测量中重建量子状态。对于广泛的纯粹和混合输入状态，我们通过模拟证明我们的方法与传统方法的功能相同的态产生了与传统方法的相同状态，并具有附加的好处，即昂贵的计算与我们的系统预装。此外，通过训练系统的测量结果，与典型的重建方法相比，我们能够证明平均保真度明显增强。当我们考虑缺少几个测量值的部分层析成像数据中，当我们考虑状态重建时，这些平均忠诚度的增强也会持续存在。我们预计，将机器智能和量子状态估计的领域结合在一起的目前结果将大大改善和加快基于层析成像的量子实验。
We build a general quantum state tomography framework that makes use of machine learning techniques to reconstruct quantum states from a given set of coincidence measurements. For a wide range of pure and mixed input states we demonstrate via simulations that our method produces functionally equivalent reconstructed states to that of traditional methods with the added benefit that expensive computations are front-loaded with our system. Further, by training our system with measurement results that include simulated noise sources we are able to demonstrate a significantly enhanced average fidelity when compared to typical reconstruction methods. These enhancements in average fidelity are also shown to persist when we consider state reconstruction from partial tomography data where several measurements are missing. We anticipate that the present results combining the fields of machine intelligence and quantum state estimation will greatly improve and speed up tomography-based quantum experiments.
