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 quan...

中文翻译：

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机器学习辅助的量子态估计

我们建立了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一致性测量中重建量子态。对于大量的纯输入和混合输入状态，我们通过仿真证明，我们的方法产生的功能与传统方法具有相同的重构状态，并具有额外的好处，即系统中会预先加载昂贵的计算。此外，通过用包括模拟噪声源的测量结果训练我们的系统，与典型的重建方法相比，我们能够证明平均保真度大大提高。当我们考虑从缺少几个测量值的部分层析成像数据进行状态重建时，平均保真度的这些增强也将持续存在。