  Obtaining accurate ground and low-lying excited states of electronic systems
is crucial in a multitude of important applications. One ab initio method for
solving the Schr\"odinger equation that scales favorably for large systems is
variational quantum Monte Carlo (QMC). The recently introduced deep QMC
approach uses ansatzes represented by deep neural networks and generates nearly
exact ground-state solutions for molecules containing up to a few dozen
electrons, with the potential to scale to much larger systems where other
highly accurate methods are not feasible. In this paper, we extend one such
ansatz (PauliNet) to compute electronic excited states. We demonstrate our
method on various small atoms and molecules and consistently achieve high
accuracy for low-lying states. To highlight the method's potential, we compute
the first excited state of the much larger benzene molecule, as well as the
conical intersection of ethylene, with PauliNet matching results of more
expensive high-level methods.
