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Quantum computers (QC) could harbor the potential to significantly advance materials simulations, particularly at the atomistic scale involving strongly correlated fermionic systems where an accurate description of quantum many-body effects…

Dynamical mean-field theory (DMFT) maps the local Green's function of the Hubbard model to that of the Anderson impurity model and thus gives an approximate solution of the Hubbard model from the solution of simpler quantum impurity model.…

Recent developments in quantum hardware and quantum algorithms have made it possible to utilize the capabilities of current noisy intermediate-scale quantum devices for addressing problems in quantum chemistry and condensed matter physics.…

Quantum Physics · Physics 2023-05-01 Steffen Backes , Yuta Murakami , Shiro Sakai , Ryotaro Arita

We present an algorithm for solving the self-consistency equations of the dynamical mean-field theory (DMFT) with high precision and efficiency at low temperatures. In each DMFT iteration, the impurity problem is mapped to an auxiliary…

Strongly Correlated Electrons · Physics 2013-06-11 D. Rost , F. Assaad , N. Blümer

We present a time-domain iteration scheme for solving the Dynamical Mean-Field Theory (DMFT) self-consistent equations using retarded Green's functions in real time. Unlike conventional DMFT approaches that operate in imaginary time or…

Strongly Correlated Electrons · Physics 2026-01-28 Chakradhar Rangi , Aadi Singh , Ka-Ming Tam

The accurate theoretical description of materials with strongly correlated electrons is a formidable challenge in condensed matter physics and computational chemistry. Dynamical Mean Field Theory (DMFT) is a successful approach that…

We study a proof-of-principle example of the recently proposed hybrid quantum-classical simulation of strongly correlated fermion models in the thermodynamic limit. In a "two-site" dynamical mean-field theory (DMFT) approach we reduce the…

Dynamical Mean Field Theory (DMFT) is one of the powerful computational approaches to study electron correlation effects in solid-state materials and molecules. Its practical applicability is, however, limited by the quantity of numerical…

Strongly Correlated Electrons · Physics 2024-12-23 Jannis Ehrlich , Daniel Urban , Christian Elsässer

Quantitative descriptions of strongly correlated materials pose a considerable challenge in condensed matter physics and chemistry. A promising approach to address this problem is quantum embedding methods. In particular, the dynamical…

Quantum Physics · Physics 2024-05-07 Rihito Sakurai , Wataru Mizukami , Hiroshi Shinaoka

For treating correlated electronic systems on quantum computers, we propose a quantum-classical hybrid scheme for dynamical mean-field theory (DMFT). In the quantum part of the scheme, we use modified quantum phase estimation (QPE) circuits…

Dynamical mean-field theory (DMFT) is a useful tool to analyze models of strongly correlated fermions like the Hubbard model. In DMFT, the lattice of the model is replaced by a single impurity site embedded in an effective bath. The…

Quantum Physics · Physics 2026-03-27 Stefan Wolf , Martin Eckstein , Michael J. Hartmann

We numerically emulate noisy intermediate-scale quantum (NISQ) devices and determine the minimal hardware requirements for two-site hybrid quantum-classical dynamical mean-field theory (DMFT). We develop a circuit recompilation algorithm…

Quantum Physics · Physics 2020-06-19 Ben Jaderberg , Abhishek Agarwal , Karsten Leonhardt , Martin Kiffner , Dieter Jaksch

Simulating quantum many-body systems is believed to be one of the most promising applications of near-term noisy quantum computers. However, in the near term, system size limitation will remain a severe barrier for applications in materials…

Strongly Correlated Electrons · Physics 2024-05-10 Anshumitra Baul , Herbert F Fotso , Hanna Terletska , Juana Moreno , Ka-Ming Tam

We present an efficient ab initio dynamical mean-field theory (DMFT) implementation for quantitative simulations in solids. Our DMFT scheme employs ab initio Hamiltonians defined for impurities comprising the full unit cell or a supercell…

Strongly Correlated Electrons · Physics 2020-03-05 Tianyu Zhu , Zhi-Hao Cui , Garnet Kin-Lic Chan

Machine learning opens new avenues for modelling correlated materials. Quantum embedding approaches, such as the dynamical mean-field theory (DMFT), provide corrections to first-principles calculations for strongly correlated materials,…

Computational Physics · Physics 2021-12-01 Evan Sheridan , Christopher Rhodes , Francois Jamet , Ivan Rungger , Cedric Weber

The developments of quantum computing algorithms and experiments for atomic scale simulations have largely focused on quantum chemistry for molecules, while their application in condensed matter systems is scarcely explored. Here we present…

Noise is often regarded as a limitation of quantum computers. In this work, we show that in the dynamical mean field theory (DMFT) approach to strongly-correlated systems, it can actually be harnessed to our advantage. Indeed, DMFT maps a…

Quantum Physics · Physics 2025-08-01 Corentin Bertrand , Pauline Besserve , Michel Ferrero , Thomas Ayral

We present clear numerical evidence for the coexistence of metallic and insulating dynamical mean field theory(DMFT) solutions in a half-filled single-band Hubbard model with bare semicircular density of states at finite temperatures.…

Strongly Correlated Electrons · Physics 2009-10-31 Jaewook Joo , Viktor Oudovenko

The Iterated Perturbation Theory (IPT) equations of the Dynamical Mean Field Theory (DMFT) for the half-filled Hubbard model, are solved on nearly real frequencies at various values of the Hubbard parameters $U$, to investigate the nature…

Strongly Correlated Electrons · Physics 2008-08-05 M. B. Fathi , S. A. Jafari

We provide a mathematical analysis of the Dynamical Mean-Field Theory, a celebrated representative of a class of approximations in quantum mechanics known as embedding methods. We start by a pedagogical and self-contained mathematical…

Mathematical Physics · Physics 2024-06-06 Éric Cancès , Alfred Kirsch , Solal Perrin-Roussel
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