Sureshbabu Shree Hari, Sajjan Manas, Oh Sangchul, Kais Sabre
School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47907, United States.
Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States.
J Chem Inf Model. 2021 Jun 16. doi: 10.1021/acs.jcim.1c00294.
Quantum machine learning algorithms, the extensions of machine learning to quantum regimes, are believed to be more powerful as they leverage the power of quantum properties. Quantum machine learning methods have been employed to solve quantum many-body systems and have demonstrated accurate electronic structure calculations of lattice models, molecular systems, and recently periodic systems. A hybrid approach using restricted Boltzmann machines and a quantum algorithm to obtain the probability distribution that can be optimized classically is a promising method due to its efficiency and ease of implementation. Here, we implement the benchmark test of the hybrid quantum machine learning on the IBM-Q quantum computer to calculate the electronic structure of typical two-dimensional crystal structures: hexagonal-boron nitride and graphene. The band structures of these systems calculated using the hybrid quantum machine learning approach are in good agreement with those obtained by the conventional electronic structure calculations. This benchmark result implies that the hybrid quantum machine learning method, empowered by quantum computers, could provide a new way of calculating the electronic structures of quantum many-body systems.
量子机器学习算法是机器学习在量子领域的扩展，由于其利用了量子特性的力量，被认为更加强大。量子机器学习方法已被用于解决量子多体系统问题，并在晶格模型、分子系统以及最近的周期性系统的精确电子结构计算中得到了验证。一种结合受限玻尔兹曼机和量子算法以获得可经典优化的概率分布的混合方法，因其效率高且易于实现而成为一种很有前景的方法。在此，我们在IBM-Q量子计算机上对混合量子机器学习进行基准测试，以计算典型二维晶体结构——六方氮化硼和石墨烯的电子结构。使用混合量子机器学习方法计算得到的这些系统的能带结构与通过传统电子结构计算得到的结果高度吻合。这一基准测试结果表明，由量子计算机赋能的混合量子机器学习方法可为计算量子多体系统的电子结构提供一种新途径。
