A hybrid method uses fixed quantum annealing states as boundary resources for classical MERA tensor networks to improve ground-state approximations without deeper quantum circuits.
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Ground-state expectation values of slow-momentum observables in QFTs can be approximated by averages over the critical fixed-point theories via fidelity-based hyperscaling relations.
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Combining non-parametric quantum states and MERA tensor networks for ground-state optimization
A hybrid method uses fixed quantum annealing states as boundary resources for classical MERA tensor networks to improve ground-state approximations without deeper quantum circuits.
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Hyperscaling of Fidelity and Operator Estimations in the Critical Manifold
Ground-state expectation values of slow-momentum observables in QFTs can be approximated by averages over the critical fixed-point theories via fidelity-based hyperscaling relations.