Scale-Invariant Quantum Machine Learning: Zero-Shot Transfer Across Molecular Dimensions via Information-Theoretic Relational Calculus
Year: 2026
Type: preprint
Abstract: This paper introduces an information-theoretic relational calculus that projects molecular energies onto a dimensionless phase space using the isolated informational potential of constituent atoms. Trained solely on H₂ and tested zero‑shot on LiH, a lightweight XGBoost model reduces absolute prediction error by 80%, eliminating dimensionality drift without deep neural networks.
Source: OSF Preprints (OSF Preprints)
Author: Concas, Massimiliano
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7156920596
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