📝 SummarXiv

Abstract

Fuzzy metric spaces, grounded in t-norms and membership functions, have been widely proposed to model uncertainty in machine learning, decision systems, and artificial intelligence. Yet these frameworks treat uncertainty as an external layer of imprecision imposed upon classical, point-like entities - a conceptual mismatch for domains where indeterminacy is intrinsic, such as quantum systems or cognitive representations. We argue that fuzzy metrics are unnecessary for modeling such uncertainty: instead, the well-established structure of complex Hilbert spaces - the foundational language of quantum mechanics for over a century - provides a natural, rigorous, and non-contradictory metric space where the ``points'' are quantum states themselves. The distance between states is given by the Hilbert norm, which directly encodes state distinguishability via the Born rule. This framework inherently captures the non-classical nature of uncertainty without requiring fuzzy logic, t-norms, or membership degrees. We demonstrate its power by modeling AI concepts as Gaussian wavefunctions and classifying ambiguous inputs via quantum overlap integrals. Unlike fuzzy methods, our approach naturally handles interference, distributional shape, and concept compositionality through the geometry of state vectors. We conclude that fuzzy metric spaces, while historically useful, are obsolete for representing intrinsic uncertainty - superseded by the more robust, predictive, and ontologically coherent framework of quantum state geometry.