Xia Rongxin, Kais Sabre
Department of Chemistry, Purdue University, West Lafayette, IN 47906, USA.
Department of Physics and Astronomy, Purdue University, West Lafayette, IN 47906, USA.
Entropy (Basel). 2020 Jul 29;22(8):828. doi: 10.3390/e22080828.
We present a hybrid quantum-classical neural network that can be trained to perform electronic structure calculation and generate potential energy curves of simple molecules. The method is based on the combination of parameterized quantum circuits and measurements. With unsupervised training, the neural network can generate electronic potential energy curves based on training at certain bond lengths. To demonstrate the power of the proposed new method, we present the results of using the quantum-classical hybrid neural network to calculate ground state potential energy curves of simple molecules such as H2, LiH, and BeH2. The results are very accurate and the approach could potentially be used to generate complex molecular potential energy surfaces.
我们提出了一种混合量子-经典神经网络，它可以通过训练来执行电子结构计算并生成简单分子的势能曲线。该方法基于参数化量子电路和测量的结合。通过无监督训练，神经网络可以基于在特定键长下的训练生成电子势能曲线。为了证明所提出的新方法的强大功能，我们展示了使用量子-经典混合神经网络来计算诸如H2、LiH和BeH2等简单分子的基态势能曲线的结果。结果非常准确，并且该方法有可能用于生成复杂的分子势能面。
