Maximally localized dynamical quantum embedding for solving many-body correlated systems

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Abstract

Quantum computing opens new avenues for modeling correlated materials, which are notoriously challenging to solve due to the presence of large electronic correlations. Quantum embedding approaches, such as dynamical mean-field theory, provide corrections to first-principles calculations for strongly correlated materials, which are poorly described at lower levels of theory. Such embedding approaches are computationally demanding on classical computing architectures and hence remain restricted to small systems, limiting the scope of their applicability. Hitherto, implementations on quantum computers have been limited by hardware constraints. Here, we derive a compact representation, where the number of quantum states is reduced for a given system while retaining a high level of accuracy. We benchmark our method for archetypal quantum states of matter that emerge due to electronic correlations, such as Kondo and Mott physics, both at equilibrium and for quenched systems. We implement this approach on a quantum emulator, demonstrating a reduction of the required number of qubits.

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Lupo, C., Jamet, F., Tse, W. H. T., Rungger, I., & Weber, C. (2021). Maximally localized dynamical quantum embedding for solving many-body correlated systems. Nature Computational Science, 1(6), 410–420. https://doi.org/10.1038/s43588-021-00090-3

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