论文标题
通过机器学习的连续时间量子步行汉密尔顿人的多参数估算
Multiparameter estimation of continuous-time Quantum Walk Hamiltonians through Machine Learning
论文作者
论文摘要
在执行从量子通信到计算的各种任务时，定义量子步行的哈密顿参数的表征至关重要。在处理量子步行的物理实现时，参数本身可能无法直接访问，因此有必要找到利用其他可观察到的替代性估计策略。在这里，我们使用在给定的进化时使用带有实验性概率的深神经网络模型，对$ n $ -neighbour互动的连续时间量子图进行表征的汉密尔顿参数的多参数估计。我们将结果与估计理论得出的边界进行比较，并发现当执行两个或三个参数的估计时，神经网络也是几乎最佳的估计量。
The characterization of the Hamiltonian parameters defining a quantum walk is of paramount importance when performing a variety of tasks, from quantum communication to computation. When dealing with physical implementations of quantum walks, the parameters themselves may not be directly accessible, thus it is necessary to find alternative estimation strategies exploiting other observables. Here, we perform the multiparameter estimation of the Hamiltonian parameters characterizing a continuous-time quantum walk over a line graph with $n$-neighbour interactions using a deep neural network model fed with experimental probabilities at a given evolution time. We compare our results with the bounds derived from estimation theory and find that the neural network acts as a nearly optimal estimator both when the estimation of two or three parameters is performed.
