论文标题
耗散量子动力学的不同机器学习方法的比较研究
A comparative study of different machine learning methods for dissipative quantum dynamics
论文作者
论文摘要
最近已经显示，仅在短期种群动态的情况下，监督的机器学习（ML）算法可以准确有效地预测耗散量子系统的长期人群动力学。在本文中，我们将22毫升模型放在了他们预测与谐波浴的两级量子系统长期动力学的能力上。这些模型包括带有线性且最常用的非线性内核的单向和双向复发，卷积和完全连接的馈送人造神经网络（ANN）和内核脊回归（KRR）。我们的结果表明，在输入轨迹的恒定长度是合适的情况下，具有非线性内核的KRR可以用作模拟长期动态的廉价而准确的方法。发现卷积门控复发单位模型是最有效的ANN模型。
It has been recently shown that supervised machine learning (ML) algorithms can accurately and efficiently predict the long-time populations dynamics of dissipative quantum systems given only short-time population dynamics. In the present article we benchmaked 22 ML models on their ability to predict long-time dynamics of a two-level quantum system linearly coupled to harmonic bath. The models include uni- and bidirectional recurrent, convolutional, and fully-connected feed-forward artificial neural networks (ANNs) and kernel ridge regression (KRR) with linear and most commonly used nonlinear kernels. Our results suggest that KRR with nonlinear kernels can serve as inexpensive yet accurate way to simulate long-time dynamics in cases where the constant length of input trajectories is appropriate. Convolutional Gated Recurrent Unit model is found to be the most efficient ANN model.
