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Trapped Ion Quantum Computing
Fragment quantum embedding using the Householder transformation: a multi-state extension based on ensembles
arXiv
Authors: Filip Cernatic, Emmanuel Fromager, Saad Yalouz
Year
2024
Paper ID
65190
Status
Preprint
Abstract Read
~2 min
Abstract Words
212
Citations
N/A
Abstract
In recent works by Yalouz et al. (J. Chem. Phys. 157, 214112, 2022) and Sekaran et al. (Phys. Rev. B 104, 035121, 2021; Computation 10, 45, 2022), Density Matrix Embedding Theory (DMET) has been reformulated through the use of the Householder transformation as a novel tool to embed a fragment within extended systems. The transformation was applied to a reference non-interacting one-electron reduced density matrix to construct fragments' bath orbitals, which are crucial for subsequent ground state calculations. In the present work, we expand upon these previous developments and extend the utilization of the Householder transformation to the description of multiple electronic states, including ground and excited states. Based on an ensemble noninteracting density matrix, we demonstrate the feasibility of achieving exact fragment embedding through successive Householder transformations, resulting in a larger set of bath orbitals. We analytically prove that the number of additional bath orbitals scales directly with the number of fractionally occupied natural orbitals in the reference ensemble density matrix. A connection with the regular DMET bath construction is also made. Then, we illustrate the use of this ensemble embedding tool in single-shot DMET calculations to describe both ground and first excited states in a Hubbard lattice model and an ab initio hydrogen system. Lastly, we discuss avenues for enhancing ensemble embedding through self-consistency and explore potential future directions.
Why This Paper Matters
- This paper contributes to the Trapped-Ion Quantum Computing research area in the Quantum Articles archive.
- It adds a 2024 reference point for readers tracking recent quantum research.
- In recent works by Yalouz et al.
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