The challenge posed by the many-body problem in quantum physics originates
from the difficulty of describing the non-trivial 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 variable number of hidden neurons. A
reinforcement-learning scheme is then demonstrated, capable of either finding
the ground-state or describing the unitary time evolution of complex
interacting quantum systems. We show that this approach achieves very high
accuracy in the description of equilibrium and dynamical properties of
prototypical interacting spins models in both one and two dimensions, thus
offering a new powerful tool to solve the quantum many-body problem.

通过机器学习的波函数系统性降低了量子物理中多体问题的复杂度，通过基于人工神经网络的变化神经元的量子状态的变分表示和强化学习方案，能够准确地描述复杂相互作用量子系统的时间演变和平衡和动态特性，为解决量子多体问题提供了新的强有力的工具。