Theoretical Physics, ETH Zurich, 8093 Zurich, Switzerland.
Quantum Architectures and Computation Group, Microsoft Research, Redmond, WA 98052, USA.
Science. 2017 Feb 10;355(6325):602-606. doi: 10.1126/science.aag2302.
The challenge posed by the many-body problem in quantum physics originates from the difficulty of describing the nontrivial correlations encoded in the exponential complexity of the many-body wave function. Here we demonstrate that systematic machine learning of the wave function can reduce this complexity to a tractable computational form for some notable cases of physical interest. We introduce a variational representation of quantum states based on artificial neural networks with a variable number of hidden neurons. A reinforcement-learning scheme we demonstrate is capable of both finding the ground state and describing the unitary time evolution of complex interacting quantum systems. Our approach achieves high accuracy in describing prototypical interacting spins models in one and two dimensions.
量子物理学中多体问题带来的挑战源于描述多体波函数指数复杂度中所蕴含的复杂非平凡关联的困难。在这里，我们证明了对于某些物理上有趣的显著情况，通过系统的机器学习对波函数进行处理，可以将这种复杂性降低到可计算的形式。我们引入了一种基于具有可变数量隐藏神经元的人工神经网络的量子态变分表示。我们展示的强化学习方案不仅能够找到基态，还能够描述复杂相互作用量子系统的幺正时间演化。我们的方法在描述一维和二维原型相互作用自旋模型方面达到了很高的精度。
