论文标题
用于量子状态估计的机器学习管道，测量不完整
Machine learning pipeline for quantum state estimation with incomplete measurements
论文作者
论文摘要
两量Qubit的系统通常采用36个投影测量值来进行高保真层析成像估计。 36个测量值的过度本质表明，估计程序可能对缺失测量值的鲁棒性。在本文中，我们通过创建堆叠机器学习模型的管道来探讨基于机器学习的量子状态估计技术对缺少测量的弹性，以进行插入，denoing和状态估计。当应用于纯状态和混合状态的模拟无噪声和嘈杂的投影测量数据时，我们通过部分测量结果证明了量子状态估计结果，这些结果以前在重建保真度中超过机器学习方法的方法和几种常规方法在资源缩放方面。值得注意的是，我们开发的模型不需要为每个缺失的测量值进行训练一个单独的模型，因此它可能适用于大量子系统的量子状态估计，由于量子系统维度的指数缩放，预处理在计算上是不可行的。
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.
