arXiv · 1807.03325
Symmetries and many-body excited states with neural-network quantum states
Abstract
Artificial neural networks have been recently introduced as a general ansatz to compactly represent many- body wave functions. In conjunction with Variational Monte Carlo, this ansatz has been applied to find Hamil- tonian ground states and their energies. Here we provide extensions of this method to study properties of ex- cited states, a central task in several many-body quantum calculations. First, we give a prescription that allows to target eigenstates of a (nonlocal) symmetry of the Hamiltonian. Second, we give an algorithm that allows to compute low-lying excited states without symmetries. We demonstrate our approach with both Restricted Boltzmann machines states and feedforward neural networks as variational wave-functions. Results are shown for the one-dimensional spin-1/2 Heisenberg model, and for the one-dimensional Bose-Hubbard model. When comparing to available exact results, we obtain good agreement for a large range of excited-states energies. Interestingly, we also find that deep networks typically outperform shallow architectures for high-energy states.
Explore related subjects
Keep this discovery
Kenny Choo, Giuseppe Carleo, Nicolas Regnault, Titus Neupert. 2018-07-09. Symmetries and many-body excited states with neural-network quantum states. https://doi.org/10.1103/physrevlett.121.167204
Cite the original work for its findings. Save a collection to share your selection of sources.