Author

Norifumi Matsumoto

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#machine learning Preprint Sep 2025

Provably scalable learning from quantum many-body data via locality

A scalable ML framework that efficiently learns from quantum many-body data by leveraging the exponential decay of correlations prevalent in noncritical systems is proposed, and it is rigorously proved that the GLQK substantially improves the sample complexity in the system size $n$ over the existing shadow kernel.

Koki Chinzei, Quoc-Hoan Tran, Norifumi Matsumoto et al. · 2 citations

Preprint Oct 2026

Circuit growth and subspace read-out in quantum-selected configuration interaction: Separating the Hartree-Fock determinant

Predicting many molecular properties requires accurate ground-state energies of molecular Hamiltonians. Quantum-selected configuration interaction (QSCI) obtains such energies by measuring an input state to select important electronic configurations and classically diagonalizing the Hamiltonian in the subspace they spa...

Ayaka Fujimoto, Norifumi Matsumoto, S. Kanasugi et al. · 0 citations