论文标题
使用机器学习算法估算一维离散时间量子步行参数
Estimation of one-dimensional discrete-time quantum walk parameters by using machine learning algorithms
论文作者
论文摘要
硬币参数的估计是使用量子步行实施更强大的量子模拟方案问题的重要组成部分。我们介绍了使用机器学习算法在其概率分布上使用机器学习算法的量子硬币参数的估计。我们表明，我们实施的模型能够将这些演变参数估计到良好的精度级别。我们还实施了一个能够同时预测多个参数的深度学习模型。由于离散时间量子步行可以用作量子模拟器，因此当从正在模拟的量子系统的概率分布中推断量子步行参数时，这些模型变得很重要。
Estimation of the coin parameter(s) is an important part of the problem of implementing more robust schemes for quantum simulation using quantum walks. We present the estimation of the quantum coin parameter used for one-dimensional discrete-time quantum walk evolution using machine learning algorithms on their probability distributions. We show that the models we have implemented are able to estimate these evolution parameters to a good accuracy level. We also implement a deep learning model that is able to predict multiple parameters simultaneously. Since discrete-time quantum walks can be used as quantum simulators, these models become important when extrapolating the quantum walk parameters from the probability distributions of the quantum system that is being simulated.
