Optimizing Entanglement Distribution Protocols: Maximizing Classical Information in Quantum Networks

2026-03-26

AI Summary

Existing entanglement distribution protocols in quantum networks are hindered by unrealistic assumptions, high computational complexity, and performance metrics disconnected from practical applications, impeding large-scale deployment. This work proposes an efficient, low-latency entanglement distribution framework that introduces Ensemble Capacity—a novel information-theoretic metric quantifying secure classical communication capability—and formulates a general optimization model without structural constraints. It further designs a dynamic programming–based hypergraph generation algorithm capable of handling arbitrary sequences of entanglement swapping and purification with continuous fidelity values. A two-tier real-time orchestration architecture, CODE, is implemented to achieve sub-second responsiveness. The proposed approach substantially enhances private information capacity while significantly reducing computational overhead, thereby meeting the dynamic operational demands of practical quantum networks.

Technology Category

• Search and Optimization: Distributed Search
• Machine Learning: Quantum Machine Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

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.

Problem

• entanglement distribution
• quantum networks
• classical information capacity
• computational complexity
• practical application alignment

Innovation

• Ensemble Capacity
• Dynamic Programming
• Entanglement Distribution
• Quantum Networks
• Hypergraph Generation