Dhawan Diksha, Metcalf Mekena, Zgid Dominika
Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States.
Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720, United States.
J Chem Theory Comput. 2021 Dec 14;17(12):7622-7631. doi: 10.1021/acs.jctc.1c00931. Epub 2021 Nov 5.
We present a two-step procedure called the dynamical self-energy mapping (DSEM) that allows us to find a sparse Hamiltonian representation for molecular problems. In the first part of this procedure, the approximate self-energy of a molecular system is evaluated using a low-level method and subsequently a sparse Hamiltonian is found that best recovers this low-level dynamic self-energy. In the second step, such a sparse Hamiltonian is used by a high-level method that delivers a highly accurate dynamical part of the self-energy that is employed in later calculations. The tests conducted on small molecular problems show that the sparse Hamiltonian parameterizations lead to very good total energies. DSEM has the potential to be used as a classical-quantum hybrid algorithm for quantum computing where the sparse Hamiltonian containing only (n) terms on a Gaussian orbital basis, where is the number of orbitals in the system, could reduce the depth of the quantum circuit by at least an order of magnitude when compared with simulations involving a full Hamiltonian.
