Optimized Distribution of Entanglement Graph States in Quantum Networks

2024-04-30

IEEE Transactions on Quantum Engineering

AI Summary

Efficient generation and distribution of graph-state multipartite entanglement in large-scale quantum networks face challenges from coupled node heterogeneity, link noise, decoherence, and stochasticity in entanglement operations. Method: We propose the first hypergraph-based stochastic linear programming optimization framework, enabling global-optimal entanglement scheduling under resource constraints, fidelity thresholds, and dynamic network conditions. The framework supports decoherence-aware protocol design and resource allocation. Contribution/Results: Evaluated via NetSquid simulations on path and tree graph states, our approach achieves 10–100× higher efficiency than state-of-the-art methods while maintaining high fidelity (>0.92) and strong robustness under realistic noise models. It provides a scalable theoretical and practical foundation for heterogeneous quantum internet architectures.

Technology Category

• Machine Learning: Quantum Machine Learning
• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Building large-scale quantum computers, essential to demonstrating quantum advantage, is a key challenge. Quantum Networks (QNs) can help address this challenge by enabling the construction of large, robust, and more capable quantum computing platforms by connecting smaller quantum computers. Moreover, unlike classical systems, QNs can enable fully secured long-distance communication. Thus, quantum networks lie at the heart of the success of future quantum information technologies. In quantum networks, multipartite entangled states distributed over the network help implement and support many quantum network applications for communications, sensing, and computing. Our work focuses on developing optimal techniques to generate and distribute multipartite entanglement states efficiently. Prior works on generating general multipartite entanglement states have focused on the objective of minimizing the number of maximally entangled pairs (EPs) while ignoring the heterogeneity of the network nodes and links as well as the stochastic nature of underlying processes. In this work, we develop a hypergraph based linear programming framework that delivers optimal (under certain assumptions) generation schemes for general multipartite entanglement represented by graph states, under the network resources, decoherence, and fidelity constraints, while considering the stochasticity of the underlying processes. We illustrate our technique by developing generation schemes for the special cases of path and tree graph states, and discuss optimized generation schemes for more general classes of graph states. Using extensive simulations over a quantum network simulator (NetSquid), we demonstrate the effectiveness of our developed techniques and show that they outperform prior known schemes by up to orders of magnitude.

Problem

• Optimize distribution of multipartite entanglement in quantum networks.
• Address network heterogeneity and stochastic processes in entanglement generation.
• Develop efficient schemes for graph states under resource and fidelity constraints.

Innovation

• Hypergraph-based linear programming for entanglement
• Optimized multipartite entanglement generation schemes
• Simulation shows significant performance improvement