AI 中文总结
研究连续变量量子态层析成像，提出基于流的QST-Flow框架，含QST-QFlow和QST-WFlow两个变体，能保持准概率归一化，通过多种态的基准测试展示其优势，为非经典玻色子系统相空间层析成像开辟新途径。
AI 中文摘要
连续变量量子态层析成像受限于在高维相空间中解析非高斯结构的成本。我们引入了QST-Flow，这是一个通过基于流的生成建模的量子态层析成像框架，它用归一化、可采样的神经密度来表示实验可获取的相空间准概率分布，而非截断密度矩阵。该框架有两个变体：QST-QFlow用单个归一化流对正的胡西米Q函数建模，QST-WFlow将变号的维格纳函数建模为两个归一化流的可训练差。此构造保持准概率归一化，并能进行精确密度评估、直接采样以及从有限相空间测量中进行重要性采样学习，无需固定网格。对非高斯猫态、二项式态、戈特斯曼-基塔耶夫-普雷斯基尔态、数态和福克态的基准测试显示了准确的单模重构、向多模态的扩展、对有噪声维格纳数据的鲁棒性以及与先前机器学习层析成像方法相比改进的重构误差。QST-Flow为非经典玻色子系统的可扩展、测量高效的相空间层析成像开辟了一条有前景的途径。
英文摘要
Continuous-variable quantum state tomography is limited by the cost of resolving non-Gaussian structure in high-dimensional phase space. We introduce QST-Flow, a quantum state tomography framework via flow-based generative modeling that represents experimentally accessible phase-space quasiprobability distributions with normalized, samplable neural densities rather than a truncated density matrix. The framework has two variants: QST-QFlow models the positive Husimi-$Q$ function with a single normalizing flow, while QST-WFlow models sign-changing Wigner functions as a trainable difference of two normalized flows. This construction preserves quasiprobability normalization and enables exact density evaluation, direct sampling, and importance-sampled learning from finite phase-space measurements without a fixed grid. Benchmarks on non-Gaussian cat, binomial, Gottesman-Kitaev-Preskill, number, and Fock states show accurate single-mode reconstructions, extension to multimode states, robustness on noisy Wigner data, and improved reconstruction error compared with prior machine-learning tomography methods. QST-Flow opens a promising route toward scalable, measurement-efficient phase-space tomography of nonclassical bosonic systems.
