CV-ADAPT-VQE with tailored symmetry-preserving pools achieves significantly shallower circuits than Hamiltonian-based VQE for bosonic lattice models in GPU classical simulations.
Hamiltonian variational ansatz without barren plateaus
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VarQEC uses a distinguishability loss as a machine-learning objective to variationally discover resource-efficient encoding circuits optimized for given noise models.
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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.
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Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction
VarQEC uses a distinguishability loss as a machine-learning objective to variationally discover resource-efficient encoding circuits optimized for given noise models.