MZeQAS accelerates quantum architecture search for VQAs by replacing full training of candidates with a zero-shot performance estimate derived from QNTK Gram-matrix convergence.
Machine learning phases of matter
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Classical shadows of local observables combined with unsupervised ML distinguish phases in the axial next-nearest-neighbor Ising model and Kitaev-Heisenberg ladder, with sample complexity scaling logarithmically in the number of features.
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Zero-shot Quantum Neural Architecture Search
MZeQAS accelerates quantum architecture search for VQAs by replacing full training of candidates with a zero-shot performance estimate derived from QNTK Gram-matrix convergence.
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Distinguishing Ordered Phases using Machine Learning and Classical Shadows
Classical shadows of local observables combined with unsupervised ML distinguish phases in the axial next-nearest-neighbor Ising model and Kitaev-Heisenberg ladder, with sample complexity scaling logarithmically in the number of features.