论文标题
贝叶斯量子状态估计的实用和高效方法
A practical and efficient approach for Bayesian quantum state estimation
论文作者
论文摘要
贝叶斯推论是量子状态断层扫描的强大范式，以有意义的和信息丰富的方式对待不确定性。然而，与复杂概率分布的采样相关的数值挑战在实际环境中缩减了贝叶斯断层扫描。在本文中，我们介绍了一种改进的贝叶斯量子状态估计方法的改进，独立的方法。利用机器学习和统计数据的进步，我们的配方依赖于高效的预处理曲柄 - 尼古斯（Nicolson）采样和伪型。我们从理论上分析了计算成本，并为实际和模拟数据集提供了推断的明确示例，这说明了现有方法的性能提高了。
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.
