Symmetric orthogonalization and probabilistic weights in resource quantification
G\"okhan Torun

TL;DR
This paper demonstrates that L"owdin symmetric orthogonalization (LSO) outperforms Gram-Schmidt in quantum resource quantification, providing more stable basis sets and introducing L"owdin weights for consistent resource measurement.
Contribution
The paper introduces L"owdin symmetric orthogonalization as a superior method for basis orthogonalization in quantum systems and proposes L"owdin weights for resource quantification, improving stability and consistency.
Findings
LSO preserves quantum state symmetry better than GSO.
L"owdin weights are non-negative and suitable for information measures.
Numerical results confirm LSO's advantages in resource characterization.
Abstract
Transforming non-orthogonal bases into orthogonal ones often compromises 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 mitigating ambiguity related to the basis choice in defining 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 procedure yields basis sets with enhanced stability, preserving the closest possible correspondence to the…
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Taxonomy
TopicsEmbedded Systems Design Techniques
