QLS applied to single- and multi-reference linearized coupled cluster shows polylog κ and sublinear sparsity on model systems, supporting prospects of exponential advantage over conjugate gradient.
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ROQAM formulates Green's function estimation via orthogonal polynomials to preserve Hessenberg structure under finite precision, enabling lower precision with depth and outperforming QSVD by orders of magnitude in resource estimates for a quantum impurity model.
A comprehensive review organizing progress at the AI-quantum information intersection from both directions.
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Quantum linear solvers for quantum chemistry: prospects of exponential quantum advantage
QLS applied to single- and multi-reference linearized coupled cluster shows polylog κ and sublinear sparsity on model systems, supporting prospects of exponential advantage over conjugate gradient.
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Estimating Green's functions with a robust quantum Arnoldi method
ROQAM formulates Green's function estimation via orthogonal polynomials to preserve Hessenberg structure under finite precision, enabling lower precision with depth and outperforming QSVD by orders of magnitude in resource estimates for a quantum impurity model.
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