A variational optimization framework computes linear classical bounds on network input/output probabilities whose violation certifies nonclassicality, finding entanglement necessary for nonclassicality in single-sender broadcast networks but not in multi-sender networks.
General parameter-shift rules for quantum gradients
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CV-ADAPT-VQE with tailored symmetry-preserving pools achieves significantly shallower circuits than Hamiltonian-based VQE for bosonic lattice models in GPU classical simulations.
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Continuous-variable ADAPT-VQE for bosonic lattice models
CV-ADAPT-VQE with tailored symmetry-preserving pools achieves significantly shallower circuits than Hamiltonian-based VQE for bosonic lattice models in GPU classical simulations.