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Design of Quantum-classical Computing Hybrid Algorithms for Materials Simulation

Noah Berthusen, Feng Zhang, Yongxin Yao, Peter P. Orth, Department of Electrical and Computer Engineering, Department of Physics and Astronomy, Ames Laboratory, Ames IA 50011

Accurately predicting properties of strongly correlated quantum materials is one of the grand challenges in many-body physics research. Classical methods for correlated materials simulation such as the state-of-the-art method (DFT+DMFT), which combines density functional theory (DFT) with dynamical mean-field theory (DMFT) are computationally costly, in particular for complex materials with multi-orbitals and strong spin-orbit coupling. This complexity is intrinsic to the many-electron problem and arises from the exponential growth of the complexity of the electronic many-body wave function. To overcome these limitations, this project aims to leverage existing noisy intermediate-scale quantum (NISQ) computer technology to achieve highly accurate total energy calculations and to simulate quantum dynamics in correlated multi-orbital quantum materials. Specifically, we are determining the ground state solution of the Gutzwiller interacting quantum embedding Hamiltonian by implementing a variational quantum eigensolver (VQE) that runs on state-of-the-art quantum cloud services of our partner company Rigetti Quantum Computing. This is feasible on NISQ devices, as it takes advantage of relatively shallow quantum circuits for error mitigation. We aim to construct a variational ansatz for the VQE that approximates the non-commuting Hamiltonian terms using a Trotter-Suzuki method. By first applying the variational method to a smaller part of the full Hubbard cluster, we can efficiently and accurately solve interacting models that are computationally too costly to be solved on classical computers.




Additional Abstract Information

Presenter: Noah Berthusen

Institution: Iowa State University

Type: Poster

Subject: Physics/Astronomy

Status: Approved


Time and Location

Session: Poster 9
Date/Time: Wed 12:00pm-1:00pm
Session Number: 6146