AL-ATCI uses active learning to identify the relevant determinant manifold in configuration-interaction impurity solvers, achieving weak scaling with bath size and reproducing exact-diagonalization accuracy for Hubbard model clusters up to size 10 and Sr2RuO4 impurities.
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Quantum DMFT framework combining Gaussian subspace state representation with compressed circuits for Green's functions, shown to converge in simulation and run on 8-qubit IBM hardware.
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A Scalable Configuration-Interaction Impurity Solver via Active Learning
AL-ATCI uses active learning to identify the relevant determinant manifold in configuration-interaction impurity solvers, achieving weak scaling with bath size and reproducing exact-diagonalization accuracy for Hubbard model clusters up to size 10 and Sr2RuO4 impurities.
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Efficient Quantum Implementation of Dynamical Mean Field Theory for Correlated Materials
Quantum DMFT framework combining Gaussian subspace state representation with compressed circuits for Green's functions, shown to converge in simulation and run on 8-qubit IBM hardware.