A practical and efficient approach for Bayesian quantum state estimation
A practical and efficient approach for Bayesian quantum state estimation
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DOI：
10.1088/1367-2630/ab8efa
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发表时间：
2020-06-01
影响因子：
3.3
通讯作者：
Lougovski, Pavel
中科院分区：
文献类型：
--
作者：
Lukens, Joseph M.;Law, Kody J. H.;Lougovski, Pavel
贝叶斯推理是量子态层析成像的一个强大范例，以有意义和信息丰富的方式处理不确定性。然而，从复杂的概率分布采样相关的数值挑战阻碍了贝叶斯层析成像在实际环境中。在这篇文章中，我们介绍了一种改进的，自包含的贝叶斯量子态估计方法。利用机器学习和统计学的进步，我们的公式依赖于高效的预处理Crank-Nicolson采样和伪似然。我们从理论上分析了计算成本，并提供了明确的例子，为实际和模拟数据集的推理，说明改进的性能与现有的方法。
Bayesian inference is a powerful paradigm for quantum state tomography, treating uncertainty in meaningful and informative ways. Yet the numerical challenges associated with sampling from complex probability distributions hampers Bayesian tomography in practical settings. In this article, we introduce an improved, self-contained approach for Bayesian quantum state estimation. Leveraging advances in machine learning and statistics, our formulation relies on highly efficient preconditioned Crank-Nicolson sampling and a pseudo-likelihood. We theoretically analyze the computational cost, and provide explicit examples of inference for both actual and simulated datasets, illustrating improved performance with respect to existing approaches.