论文标题
关于通过机器学习的量子状态重建的实验可行性
On the experimental feasibility of quantum state reconstruction via machine learning
论文作者
论文摘要
我们确定基于机器学习的量子状态重建方法的资源缩放，在推理和培训方面，对于限制在纯状态时，最多四个Qubit的系统。此外，我们检查了低计算系统中可能遇到的低计数制度中的系统性能。最后，我们在IBM Q量子计算机上实现了量子状态重建方法，并与不受约束和受约束的MLE状态重建进行比较。
We determine the resource scaling of machine learning-based quantum state reconstruction methods, in terms of inference and training, for systems of up to four qubits when constrained to pure states. Further, we examine system performance in the low-count regime, likely to be encountered in the tomography of high-dimensional systems. Finally, we implement our quantum state reconstruction method on an IBM Q quantum computer, and compare against both unconstrained and constrained MLE state reconstruction.
