机器学习辅助的量子态估计
Machine learning assisted quantum state estimation
摘要：
我们建立了一个通用的量子态层析成像框架，利用机器学习技术从一组给定的符合测量中重建量子态。对于广泛的纯和混合输入状态，我们通过模拟证明，我们的方法产生功能等效的重构状态的传统方法的额外好处，昂贵的计算是前加载与我们的系统。此外，通过使用包括模拟噪声源的测量结果来训练我们的系统，与典型的重建方法相比，我们能够证明显着增强的平均保真度。当我们考虑从部分断层扫描数据中缺失几个测量值的状态重建时，平均保真度的这些增强也被证明是持久的。我们预计，目前的结果结合机器智能和量子态估计领域将大大提高和加速基于断层扫描的量子实验。
英文摘要：
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.
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