AI 中文总结
本文提出基于归一化流的贝叶斯参数估计框架，用于含噪声量子态的参数估计与相关性分析，并在光学猫态实验中验证其有效性，为实验和理论提供参数优化指导。
AI 中文摘要
为了提取实验含噪声量子态的完整信息，以及不同物理参数之间的相关性，我们开发了一种高效的机器学习辅助框架，采用基于归一化流的贝叶斯参数估计（BPE）。通过将物理上可解释的参数向量 ${s}$ 嵌入量子态拟设 $\rho({s})$ 中，相应的参数分布 $p({s}|D)$ 由 BPE 基于可用的实验数据 $D$ 推导得出。BPE 使我们能够进行参数相关性分析，为实验参数及相关噪声源如何影响系统的量子特性提供有力见解。作为示例，我们使用两种不同的物理拟设，在光学猫态实验中展示了我们框架的灵活性，其中将含噪声的单光子态添加到不纯的压缩态中。通过利用 Wigner 函数负值、光子添加分数、噪声分数和泵浦功率之间的相关性，我们的框架为生成含噪声非高斯态的复杂实验机制提供了解释。通过利用基于归一化流的神经网络和贝叶斯不确定性估计，并自然地纳入物理和实验约束，后验学习中所推断出的宝贵信息为实验学家和理论学家提供了指导，以识别有前景的参数来增强所需的特性。
英文摘要
To extract complete information about experimental noisy quantum states, as well as correlations among different physical parameters, we develop an efficient machine learning-assisted framework with the normalizing flow based Bayesian parameter estimation (BPE). By embedding a physically interpretable parameter vector, ${s}$, into the quantum state ansatzes, $ρ({s})$, the corresponding parameter distribution $p({s}|D)$ is induced from BPE, based on the available experimental data $D$. The BPE allows us to perform parameter correlation analyses, providing powerful insights about how experimental parameters and related noise sources affect quantum features of the system. As an example, the flexibility of our framework is demonstrated using two different physical ansatzes on the optical cat state experiments, where a noisy single-photon state is added to impure squeezed state. With correlations among the Wigner function negativity, photon-addition fraction, noisy fraction, and pump powers, our framework gives interpretations for complicated experimental mechanisms in generating noisy non-Gaussian states. By leveraging normalizing flow-based neural networks and Bayesian uncertainty estimation, along with physical and experimental constraints incorporated naturally, the valuable information inferred in the posterior learning provides guidelines for experimentalists, as well as theorists, in identifying promising parameters to enhance desirable features.
Comments5 pages, 4 figures, with Supplementary Information
