Symmetric orthogonalization and probabilistic weights in resource quantification

Abstract: Transforming non-orthogonal bases into orthogonal ones often sacrifices essential properties or physical meaning in quantum systems. Here, we demonstrate that L\"owdin symmetric orthogonalization (LSO) outperforms the widely used Gram-Schmidt orthogonalization (GSO) in characterizing and quantifying quantum resources, with particular emphasis on coherence and superposition. We employ LSO both to construct an orthogonal basis from a non-orthogonal one and to obtain a non-orthogonal basis from an orthogonal set, thereby avoiding any ambiguity related to the basis choice for quantum coherence. Unlike GSO, which depends on the ordering of input states, LSO applies a symmetric transformation that treats all vectors equally and minimizes deviation from the original basis. This approach generates basis sets with enhanced stability and physical relevance, facilitating the analysis of superpositions in non-orthogonal quantum states. Building on LSO, we also introduce L\"owdin weights -- probabilistic weights for non-orthogonal representations that provide a consistent measure of resource content. These weights further enable basis-independent quantification of coherence and state delocalization through information-theoretic measures such as entropy and participation ratios. Our theoretical and numerical analyses confirm LSO's superior preservation of quantum state symmetry and resource characteristics, underscoring the critical role of orthogonalization methods and L\"owdin weights in resource theory frameworks.