Accurate computation of quantum excited states with neural networks

Affiliations
  • 1Google DeepMind, London N1C 4DJ, UK.
  • 2Department of Physics, Imperial College London, South Kensington Campus, London SW7 2AZ, UK.
  • 3Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 01238, USA.
  • 4Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 01239, USA.

Published on:

Abstract

We present an algorithm to estimate the excited states of a quantum system by variational Monte Carlo, which has no free parameters and requires no orthogonalization of the states, instead transforming the problem into that of finding the ground state of an expanded system. Arbitrary observables can be calculated, including off-diagonal expectations, such as the transition dipole moment. The method works particularly well with neural network ansätze, and by combining this method with the FermiNet and Psiformer ansätze, we can accurately recover excitation energies and oscillator strengths on a range of molecules. We achieve accurate vertical excitation energies on benzene-scale molecules, including challenging double excitations. Beyond the examples presented in this work, we expect that this technique will be of interest for atomic, nuclear, and condensed matter physics.