Quantum-Channel Matrix Optimization for Holevo Bound Enhancement

TL;DR

This paper introduces a gradient ascent algorithm to optimize quantum channels, significantly increasing the Holevo bound and enhancing the capacity of quantum communication systems.

Contribution

A unified projected gradient ascent method for quantum channel optimization that improves Holevo bound calculations with detailed complexity analysis.

Findings

Optimized quantum channels yield higher Holevo bounds.

The proposed algorithm outperforms input ensemble optimization.

Simulation results confirm improved information transmission capacity.

Abstract

Quantum communication holds the potential to revolutionize information transmission by enabling secure data exchange that exceeds the limits of classical systems. One of the key performance metrics in quantum information theory, namely the Holevo bound, quantifies the amount of classical information that can be transmitted reliably over a quantum channel. However, computing and optimizing the Holevo bound remains a challenging task due to its dependence on both the quantum input ensemble and the quantum channel. In order to maximize the Holevo bound, we propose a unified projected gradient ascent algorithm to optimize the quantum channel given a fixed input ensemble. We provide a detailed complexity analysis for the proposed algorithm. Simulation results demonstrate that the proposed quantum channel optimization yields higher Holevo bounds than input ensemble optimization.