  The excitation ansatz for tensor networks is a powerful tool for simulating
the low-lying quasiparticle excitations above ground states of strongly
correlated quantum many-body systems. Recently, the two-dimensional tensor
network class of infinite entangled pair states gained new ground state
optimization methods based on automatic differentiation, which are at the same
time highly accurate and simple to implement. Naturally, the question arises
whether these new ideas can also be used to optimize the excitation ansatz,
which has recently been implemented in two dimensions as well. In this paper,
we describe a straightforward way to reimplement the framework for excitations
using automatic differentiation, and demonstrate its performance for the
Hubbard model at half filling.
