The Science

Accurately simulating the ground states of molecular electronic systems is at the heart of theoretical chemistry and materials science but is challenging as chemical complexity increases. Researchers combined double unitary coupled cluster (DUCC) theory and the adaptive, problem-tailored variational quantum eigensolver (ADAPT-VQE) for quantum simulations of chemistry. This approach, targeted for use on future quantum devices, showed increased accuracy compared to more traditional simulation approaches without increasing the computational load on the quantum computer. The results highlight the value of using DUCC to improve quantum simulations of chemical and materials processes.

The Impact

Classical approaches to modeling molecular electronic system ground states often fail when electrons are strongly correlated, which is often the case in many materials with useful electronic and magnetic properties. As quantum physics underlies these properties, quantum computing is a promising option for accurately simulating these systems. Applying DUCC to these problems, which simplifies the construction of Hamiltonian representations, enabled the team to increase the accuracy of the simulations without requiring significantly more computing power—key for using quantum processors with limited numbers of qubits.

Summary

Researchers combined recently developed DUCC theory with ADAPT-VQE to explore the accuracy of unitary downfolded Hamiltonians for quantum simulation of chemistry. They benchmarked the ability of DUCC effective Hamiltonians to recover dynamical correlation energy outside of an active space. They considered the effects of strong correlation, commutator truncation, higher-body terms, and approximate external amplitudes on the accuracy of these effective Hamiltonians. When combining these DUCC Hamiltonians with ADAPT-VQE, they observed a similar convergence of the ground state when compared to bare active space Hamiltonians. These results demonstrate that DUCC Hamiltonians provide increased accuracy without increasing the load on the quantum processor.

Contact

Karol Kowalski, Pacific Northwest National Laboratory, Karol.Kowalski@pnnl.gov 

Funding

L.W.B., D.C., N.P.B., and K.K. acknowledge support from the “Embedding Quantum Computing into Many-body Frameworks for Strongly Correlated Molecular and Materials Systems” project, funded by the U.S. Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences, the Division of Chemical Sciences, Geosciences, and Biosciences (under FWP 72689). N.P.B. also acknowledges support from the Quantum Algorithms and Architecture for Domain Science Initiative (QuAADS), a Laboratory Directed Research and Development Program at PNNL. E.B. and S.E.E. acknowledge support by the DOE, Office of Science, Office of Advanced Scientific Computing Research, under Award Number DE-SC0025430. N.J.M. acknowledges support from the DOE, under Award Number DE-SC0024619. The authors thank Advanced Resource Computing at Virginia Tech for use of computational resources.