Solving the quantum many-body problem with artificial neural networks
Solving the quantum many-body problem with artificial neural networks
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DOI：
10.1126/science.aag2302
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发表时间：
2017-02-10
期刊：
影响因子：
56.9
通讯作者：
Troyer, Matthias
中科院分区：
文献类型：
--
作者：
Carleo, Giuseppe;Troyer, Matthias
量子物理中的多体问题提出的挑战源于描述多体波函数的指数复杂性中所编码的非平凡关联的困难。在这里，我们演示了波函数的系统机器学习可以将这种复杂性降低到对于一些值得注意的物理感兴趣的情况的易于处理的计算形式。我们介绍了一种基于人工神经网络的量子态的变分表示，该网络具有可变数目的隐藏神经元。我们证明了一种强化学习方案，它既能找到基态又能描述复杂相互作用量子系统的么正时间演化。我们的方法在描述一维和二维的原型相互作用自旋模型时达到了很高的精度。
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.