  Solving the Anderson impurity model typically involves a two-step process,
where one first calculates the ground state of the Hamiltonian, and then
computes its dynamical properties to obtain the Green's function. Here we
propose a hybrid classical/quantum algorithm where the first step is performed
using a classical computer to obtain the tensor network ground state as well as
its quantum circuit representation, and the second step is executed on the
quantum computer to obtain the Green's function. Our algorithm exploits the
efficiency of tensor networks for preparing ground states on classical
computers, and takes advantage of quantum processors for the evaluation of the
time evolution, which can become intractable on classical computers. We
demonstrate the algorithm using 20 qubits on a quantum computing emulator for
SrVO3 with a multi-orbital Anderson impurity model within the dynamical mean
field theory. The tensor network based ground state quantum circuit preparation
algorithm can also be performed for up to 40 qubits with our available
computing resources, while the state vector emulation of the quantum algorithm
for time evolution is beyond what is accessible with such resources. We show
that, provided the tensor network calculation is able to accurately obtain the
ground state energy, this scheme does not require a perfect reproduction of the
ground state wave function on the quantum circuit to give an accurate Green's
function. This hybrid approach may lead to quantum advantage in materials
simulations where the ground state can be computed classically, but where the
dynamical properties cannot.
