Science· 2017Q1
Solving the quantum many-body problem with artificial neural networks
- 2,252citations
- Q1SCImago
- 2017year
Short summary
Artificial neural networks can learn quantum wave functions, reducing computational complexity for interacting quantum systems.
AI-generated from the title and abstract; the full text is not read.
Key points
- Artificial neural networks offer a variational representation for quantum states.
- A reinforcement-learning scheme can find ground states and describe time evolution.
- This approach reduces the computational complexity of quantum many-body systems.
- High accuracy was achieved for one- and two-dimensional interacting spin models.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. Science, 2017 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Atomic and Molecular Physics, and Optics
Atomic and Molecular Physics, and OpticsPhysics and Astronomy