Accurate computation of quantum excited states with neural networks
Citations Over TimeTop 1% of 2024 papers
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.
Related Papers
- → Mayer’s orthogonalization: relation to the Gram-Schmidt and Löwdin’s symmetrical scheme(2012)13 cited
- → Fermionic quantum orthogonalizations I(2015)2 cited
- → Reverse, Left and Right Orthogonalization(2003)
- → High-Speed Pseudo-Orthogonalization for the Car-Parrinello Method(2007)
- Applications of Local Orthogonalization in Large Parallel Computation(2009)