The paper introduces a Riemannian gradient descent method, with a derived gradient formula, to compute lower bounds on the Holevo capacity of general quantum channels, with numerical demonstrations.
Unified Framework for Calculating Convex Roof Resource Measures
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abstract
Quantum resource theories (QRTs) provide a comprehensive and practical framework for the analysis of diverse quantum phenomena. A fundamental task within QRTs is the quantification of resources inherent in a given quantum state. In this letter, we introduce a unified computational framework for a class of widely utilized quantum resource measures, derived from convex roof extensions. We establish that the computation of these convex roof resource measures can be reformulated as an optimization problem over a Stiefel manifold, which can be further unconstrained through polar projection. Compared to existing methods employing semi-definite programming (SDP), gradient-based techniques or seesaw strategy, our approach not only demonstrates superior computational efficiency but also maintains applicability across various scenarios within a streamlined workflow. We substantiate the efficacy of our method by applying it to several key quantum resources, including entanglement, coherence, and magic states. Moreover, our methodology can be readily extended to other convex roof quantities beyond the domain of resource theories, suggesting broad applicability in the realm of quantum information theory.
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Riemannian Optimization for Holevo Capacity
The paper introduces a Riemannian gradient descent method, with a derived gradient formula, to compute lower bounds on the Holevo capacity of general quantum channels, with numerical demonstrations.