论文标题
基于维度自适应机器学习的量子状态重建
Dimension-adaptive machine-learning-based quantum state reconstruction
论文作者
论文摘要
我们介绍了一种使用基于机器学习的重建系统在$ M $ Qubits（其中$ M \ geq n $）上训练的基于机器学习的重建系统，以在$ n $ Qubits的系统上执行量子状态重建方法。这种方法消除了将正在考虑的系统的维度与用于训练的模型的维度完全匹配的必要性。我们通过使用基于机器学习的方法在一个，两个量子和三个量子的随机采样系统上进行量子状态重建来证明我们的技术，该方法专为包含至少一个额外量子的系统培训。基于机器学习方法所需的重建时间比训练时间更有利。因此，该技术可以通过利用单个神经网络进行维度变化状态重建来提供总体节省的资源，从而消除了为每个希尔伯特空间培训专用的机器学习系统的需求。
We introduce an approach for performing quantum state reconstruction on systems of $n$ qubits using a machine-learning-based reconstruction system trained exclusively on $m$ qubits, where $m\geq n$. This approach removes the necessity of exactly matching the dimensionality of a system under consideration with the dimension of a model used for training. We demonstrate our technique by performing quantum state reconstruction on randomly sampled systems of one, two, and three qubits using machine-learning-based methods trained exclusively on systems containing at least one additional qubit. The reconstruction time required for machine-learning-based methods scales significantly more favorably than the training time; hence this technique can offer an overall savings of resources by leveraging a single neural network for dimension-variable state reconstruction, obviating the need to train dedicated machine-learning systems for each Hilbert space.
