论文标题
关于密切相关的电子问题的机器学习和数据科学的观点
A perspective on machine learning and data science for strongly correlated electron problems
论文作者
论文摘要
相关电子问题的数值方法取得了很大的成功，但仍受到多个瓶颈的限制，包括系统大小的高阶多项式或指数缩放，长期自相关时间，识别新型相位的挑战以及fermion符号问题。机器学习中的方法（ML），人工智能和数据科学有望帮助解决这些局限性，并在密切相关的量子系统模拟中打开新的边界。在本文中，我们回顾了这一领域的一些进展。我们首先在经典模型的背景下检查这些方法，在这些模型的背景下，它们的基础和应用可以轻松地进行说明和标准。然后，我们讨论ML方法启用科学发现的案例。最后，我们将研究它们在最先进的量子多体型方法（如量子蒙特卡洛）中加速模型解决方案中的应用，并讨论潜在的未来研究方向。
Numerical approaches to the correlated electron problem have achieved considerable success, yet are still constrained by several bottlenecks, including high order polynomial or exponential scaling in system size, long autocorrelation times, challenges in recognizing novel phases, and the Fermion sign problem. Methods in machine learning (ML), artificial intelligence, and data science promise to help address these limitations and open up a new frontier in strongly correlated quantum system simulations. In this paper, we review some of the progress in this area. We begin by examining these approaches in the context of classical models, where their underpinnings and application can be easily illustrated and benchmarked. We then discuss cases where ML methods have enabled scientific discovery. Finally, we will examine their applications in accelerating model solutions in state-of-the-art quantum many-body methods like quantum Monte Carlo and discuss potential future research directions.
