Constructing exact representations of quantum many-body systems with deep neural networks

Giuseppe Carleo, Yusuke Nomura, Masatoshi Imada

研究成果: Article査読

109 被引用数 (Scopus)


Obtaining accurate properties of many-body interacting quantum matter is a long-standing challenge in theoretical physics and chemistry, rooting into the complexity of the many-body wave-function. Classical representations of many-body states constitute a key tool for both analytical and numerical approaches to interacting quantum problems. Here, we introduce a technique to construct classical representations of many-body quantum systems based on artificial neural networks. Our constructions are based on the deep Boltzmann machine architecture, in which two layers of hidden neurons mediate quantum correlations. The approach reproduces the exact imaginary-time evolution for many-body lattice Hamiltonians, is completely deterministic, and yields networks with a polynomially-scaling number of neurons. We provide examples where physical properties of spin Hamiltonians can be efficiently obtained. Also, we show how systematic improvements upon existing restricted Boltzmann machines ansatze can be obtained. Our method is an alternative to the standard path integral and opens new routes in representing quantum many-body states.

ジャーナルNature communications
出版ステータスPublished - 2018 12月 1

ASJC Scopus subject areas

  • 化学一般
  • 生化学、遺伝学、分子生物学一般
  • 物理学および天文学一般


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