ArticleACS omega2026
Physics-Informed Machine Learning Correction of Variational Quantum Eigensolver Energies for Molecular Ground States.
Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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2 authors.
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Abstract
The variational quantum Eigensolver (VQE) provides a scalable route to molecular ground-state energies on near-term quantum hardware, but its accuracy degrades rapidly as system size grows because hardware-efficient ansätze cannot span the full correlation subspace. We introduce a physics-informed ridge regression correction that is trained on the difference between VQE and Hartree-Fock (HF) energies and applied as a postprocessing step requiring no additional quantum resources. The feature setthe VQE-HF energy gap Δ
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