Two-qubit systems typically employ 36 projective measurements for high-fidelity tomographic estimation. The overcomplete nature of the 36 measurements suggests possible robustness of the estimation procedure to missing measurements. In this paper, we explore the resilience of machine-learning-based quantum state estimation techniques to missing measurements by creating a pipeline of stacked machine learning models for imputation, denoising, and state estimation. When applied to simulated noiseless and noisy projective measurement data for both pure and mixed states, we demonstrate quantum state estimation from partial measurement results that outperforms previously developed machine-learning-based methods in reconstruction fidelity and several conventional methods in terms of resource scaling. Notably, our developed model does not require training a separate model for each missing measurement, making it potentially applicable to quantum state estimation of large quantum systems where preprocessing is computationally infeasible due to the exponential scaling of quantum system dimension.

双量子位系统通常采用 36 个投影测量来进行高保真断层扫描估计。36 次测量的过完备性表明估计程序对缺失测量可能具有鲁棒性。在本文中，我们通过创建用于插补、去噪和状态估计的堆叠机器学习模型管道，探索基于机器学习的量子状态估计技术对缺失测量的弹性。当应用于纯态和混合态的模拟无噪声和有噪声投影测量数据时，我们展示了部分测量结果的量子状态估计，其在重建保真度方面优于先前开发的基于机器学习的方法，在资源缩放方面优于几种传统方法。尤其，