Solving the quantum many-body problem with artificial neural networks
ETH Zurich · Microsoft (United States)
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…
Citation impact
- FWCI
- 118.56
- Percentile
- 100%
- References
- 57
Authors
2Topics & keywords
- Unitary state
- Quantum
- Quantum machine learning
- Computer science
- Artificial neural network
- Reinforcement learning
- Wave function
- Spins