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

Verma, Shreya [University of Chicago, IL (United States)] (ORCID:000900078560846X); Mitra, Abhishek [University of Chicago, IL (United States)] (ORCID:0000000229749600); Wang, Qiaohong [University of Chicago, IL (United States)] (ORCID:0009000947909661); D’Cunha, Ruhee [University of Chicago, IL (United States)] (ORCID:0000000218665786); Jangid, Bhavnesh [University of Chicago, IL (United States)] (ORCID:0000000160484981); Hennefarth, Matthew R. [University of Chicago, IL (United States)] (ORCID:0000000266012253); Agarawal, Valay [University of Chicago, IL (United States)] (ORCID:0000000329898013); Otis, Leon [University of Chicago, IL (United States)]; Haldar, Soumi [University of Chicago, IL (United States)] (ORCID:0000000252120106); Hermes, Matthew R. [University of Chicago, IL (United States)] (ORCID:0000000178072950); Gagliardi, Laura [University of Chicago, IL (United States)] (ORCID:0000000152271396)

doi:10.1021/acs.chemrev.5c00486

DOE OSTI · 3011770

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

View original source ↗Read source document ↗Export metadata ↓

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. Here, 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.

Explore related subjects

Algorithms Electron correlation Embedding Quantum mechanics Wave function

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Verma, Shreya [University of Chicago, IL (United States)] (ORCID:000900078560846X), Mitra, Abhishek [University of Chicago, IL (United States)] (ORCID:0000000229749600), Wang, Qiaohong [University of Chicago, IL (United States)] (ORCID:0009000947909661), D’Cunha, Ruhee [University of Chicago, IL (United States)] (ORCID:0000000218665786), Jangid, Bhavnesh [University of Chicago, IL (United States)] (ORCID:0000000160484981), Hennefarth, Matthew R. [University of Chicago, IL (United States)] (ORCID:0000000266012253), Agarawal, Valay [University of Chicago, IL (United States)] (ORCID:0000000329898013), Otis, Leon [University of Chicago, IL (United States)], Haldar, Soumi [University of Chicago, IL (United States)] (ORCID:0000000252120106), Hermes, Matthew R. [University of Chicago, IL (United States)] (ORCID:0000000178072950), Gagliardi, Laura [University of Chicago, IL (United States)] (ORCID:0000000152271396). 2026-01-02. Multireference Embedding and Fragmentation Methods for Classical and Quantum Computers: From Model Systems to Realistic Applications. https://doi.org/10.1021/acs.chemrev.5c00486

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

Particle Tracking Methods for Battery Precipitation Reactions

Precipitation and deposition reactions at solid–liquid interfaces play a key role in a number of battery chemistries, including Li-ion, so-called “anode free” batteries, zinc-based battery chemistries, and lithium–sulfur, among others. Although models with heterogeneous nucleation and growth phenomena are present in the literature, papers have not to date provided much detail on the numerical algorithms used to track the temporal evolution of the particle size distribution of deposits on electrode surfaces. In this paper we examine several approaches to discretize and track the particle size distribution, demonstrating that common approaches lead to anomalous flattening of the particle size distribution. We conclude by presenting an algorithm that preserves the appropriate particle size distribution during particle growth.

Algorithms↗

DOE OSTI2026

Harnessing Quantum Computing for Energy Materials: Opportunities and Challenges

Developing high-performance materials is critical for diverse energy applications to increase efficiency, improve sustainability and reduce costs. Classical computational methods have enabled important breakthroughs in energy materials development, but they face scaling and time-complexity limitations, particularly for high-dimensional or strongly correlated material systems. Quantum computing (QC) promises to offer a paradigm shift by exploiting quantum bits with their superposition and entanglement to address challenging problems intractable for classical approaches. This Perspective discusses the opportunities in leveraging QC to advance energy materials research and the challenges QC faces in solving complex and high-dimensional problems. We present cases on how QC, when combined with classical computing methods, can be used for the design and simulation of practical energy materials. We also outline the outlook for error-corrected, fault-tolerant QC capable of achieving predictive accuracy and quantum advantage for complex material systems.

Algorithms↗

DOE OSTI2025

Automated Construction of Artificial Lattice Structures with Designer Electronic States

Manipulating matter with a scanning tunneling microscope (STM) enables the creation of atomically defined artificial structures that host designer quantum states. However, the time-consuming nature of the manipulation process, coupled with the sensitivity of the STM tip, constrains the exploration of diverse configurations and limits the size of the designed features. In this study, we present a reinforcement learning (RL)-based framework for creating artificial structures by spatially manipulating carbon monoxide (CO) molecules on a copper substrate by using the STM tip. The automated workflow combines molecule detection and manipulation, employing deep-learning-based object detection to locate CO molecules and linear assignment algorithms to allocate these molecules to designated target sites. We initially perform molecule maneuvering based on randomized parameter sampling for sample bias, tunneling current set point, and manipulation speed. This data set is then structured into an action trajectory used to train an RL agent. The model is subsequently deployed on the STM for real-time fine-tuning of the manipulation parameters during structure construction. Our approach incorporates path-planning protocols coupled with active drift compensation to enable atomically precise fabrication of structures with significantly reduced human input while realizing larger-scale artificial lattices with the desired electronic properties. Furthermore, using our approach, we demonstrate the automated construction of an extended artificial graphene lattice and confirm the existence of a characteristic Dirac point in its electronic structure. Further challenges regarding the RL-based structural assembly scalability are discussed.

Algorithms↗