Multireference Embedding and Fragmentation Methods for Classical and Quantum Computers: from Model Systems to Realistic Applications

TL;DR

This paper reviews recent advances in multireference embedding and fragmentation methods, highlighting their potential to enable accurate quantum chemistry modeling of large systems on classical and quantum computers.

Contribution

It introduces recent developments in multireference density matrix embedding and localized active space methods, bridging classical and quantum computational approaches.

Findings

Classical embedding methods have been extended to quantum computing.

Recent algorithms improve modeling of complex molecules and materials.

Potential for multireference methods to be applied to larger systems.

Abstract

One of the primary challenges in quantum chemistry is the accurate modeling of strong electron correlation. While multireference methods effectively capture such correlation, their steep scaling with system size prohibits their application to large molecules and extended materials. Quantum embedding offers a promising solution by partitioning complex systems into manageable subsystems. In this review, we highlight recent advances in multireference density matrix embedding and localized active space self-consistent field approaches for complex molecules and extended materials. We discuss both classical implementations and the emerging potential of these methods on quantum computers. By extending classical embedding concepts to the quantum landscape, these algorithms have the potential to expand the reach of multireference methods in quantum chemistry and materials.