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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Hadamard states exhibit higher average multipartite entanglement than Haar-typical states via purity of balanced bipartitions, with hypergraph states (real alternating-sign coefficients) being especially promising for maximal entanglement due to simplicity and sampling likelihood.
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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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Multipartite entanglement of random states of qubits
Hadamard states exhibit higher average multipartite entanglement than Haar-typical states via purity of balanced bipartitions, with hypergraph states (real alternating-sign coefficients) being especially promising for maximal entanglement due to simplicity and sampling likelihood.