Anderson impurity model solver integrating tensor network methods with quantum computing*

One of the most widely used methods to describe strongly correlated materials is the dynamical mean-field theory (DMFT). Central to DMFT is the evaluation of the Green’s function of an effective Anderson impurity model (AIM), a typically twostep process where one first calculates the ground state of the Hamiltonian, and then computes its dynamical properties to obtain the Green's function. In this talk, we present a hybrid quantum/classical algorithm where the first step makes use of tensor network solvers to compute the ground state for the AIM system as well as a quantum circuit representation using classical computing resources. The second step is then performed on a quantum computer to obtain the Green's function, taking advantage of quantum processors for the evaluation of the time evolution, which can become intractable on classical computers. We demonstrate the algorithm using twenty qubits on a quantum computing emulator for SrVO3 with a multi-orbital Anderson impurity model within the dynamical mean field theory. Provided the tensor network calculation can accurately obtain the ground state energy, this scheme does not require perfect reproduction of the ground state wave function by the quantum circuit to give an accurate Green's function.