Optimizing Entanglement Distribution Protocols: Maximizing Classical Information in Quantum Networks

TL;DR

This paper introduces a new framework for optimizing entanglement distribution in quantum networks, focusing on maximizing secure classical information transfer with efficient algorithms and system-level orchestration.

Contribution

It presents the Ensemble Capacity metric, a generalized formulation for entanglement distribution, and a DP-based hypergraph algorithm integrated into the CODE system for real-time network optimization.

Findings

The Ensemble Capacity metric effectively quantifies secure classical information.

The DP-based algorithm preserves continuous fidelities and reduces computational complexity.

The CODE framework achieves near-real-time responsiveness and higher private information capacity.

Abstract

Efficient entanglement distribution is the foundational challenge in realizing large-scale Quantum Networks. However, state-of-the-art solutions are frequently limited by restrictive operational assumptions, prohibitive computational complexities, and performance metrics that misalign with practical application needs. To overcome these barriers, this paper addresses the entanglement distribution problem by introducing four pivotal advances. First, recognizing that the primary application of quantum communication is the transmission of private information, we derive the Ensemble Capacity (EC), a novel metric that explicitly quantifies the secure classical information enabled by the entanglement distribution. Second, we propose a generalized mathematical formulation that removes legacy structural restrictions in the solution space. Our formulation supports an unconstrained, arbitrary sequencing of entanglement swapping and purification. Third, to efficiently navigate the resulting combinatorial optimization space, we introduce a novel Dynamic Programming (DP)-based hypergraph generation algorithm. Unlike prior methods, our approach avoids artificial fidelity quantization, preserving exact, continuous fidelities while proactively pruning sub-optimal trajectories. Finally, we encapsulate these algorithmic solutions into CODE, a system-level, two-tiered orchestration framework designed to enable near-real-time network responsiveness. Extensive evaluations confirm that our DP-driven architecture yields superior private classical information capacity and significant reductions in computational complexity, successfully meeting the strict sub-second latency thresholds required for dynamic QN operation.