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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QCPP implements polynomial transformations of Hamiltonians via stochastic mixtures of unitary channels, achieving a tunable tradeoff between query and sample complexity.
Trotter error cancellation in nanographene simulations reduces circuit depth by about 10x for quantum phase estimation of energy gaps to chemical accuracy in the Pariser-Parr-Pople model.
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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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Quantum Channel Polynomial Processing
QCPP implements polynomial transformations of Hamiltonians via stochastic mixtures of unitary channels, achieving a tunable tradeoff between query and sample complexity.
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Quantum simulation of nanographenes and Trotter error cancellation
Trotter error cancellation in nanographene simulations reduces circuit depth by about 10x for quantum phase estimation of energy gaps to chemical accuracy in the Pariser-Parr-Pople model.