A Framework for Distributed Resource Allocation in Quantum Networks

2025-10-10

AI Summary

In quantum internet scenarios supporting multiple concurrent applications, distributed resource allocation must jointly optimize entanglement generation rate and fidelity while addressing high control overhead, quantum memory decoherence, and network congestion. Method: We propose the first fully decentralized, feedback-based coordination framework—relying solely on local state information and limited classical communication to eliminate global signaling overhead. We formulate the Quantum Network Utility Maximization (QNUM) problem and design QPrimal-Dual, a provably convergent distributed algorithm integrating dual decomposition, local estimation of global quantities, and lightweight congestion control. Results: Simulations demonstrate that our approach significantly outperforms existing baselines in scalability, robustness against communication delays and decoherence, and overall performance, while remaining compatible with realistic quantum network architectures.

Technology Category

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

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

We introduce a distributed resource allocation framework for the Quantum Internet that relies on feedback-based, fully decentralized coordination to serve multiple co-existing applications. We develop quantum network control algorithms under the mathematical framework of Quantum Network Utility Maximization (QNUM), where utility functions quantify network performance by mapping entanglement rate and quality into a joint optimization objective. We then introduce QPrimal-Dual, a decentralized, scalable algorithm that solves QNUM by strategically placing network controllers that operate using local state information and limited classical message exchange. We prove global asymptotic stability for concave, separable utility functions, and provide sufficient conditions for local stability for broader non-concave cases. To reduce control overhead and account for quantum memory decoherence, we also propose schemes that locally approximate global quantities and prevent congestion in the network. We evaluate the performance of our approach via simulations in realistic quantum network architectures. Results show that QPrimalDual significantly outperforms baseline allocation strategies, scales with network size, and is robust to latency and decoherence. Our observations suggest that QPrimalDual could be a practical, high-performance foundation for fully distributed resource allocation in quantum networks.

Problem

• Allocating quantum network resources for multiple applications
• Optimizing entanglement rate and quality jointly
• Developing decentralized scalable quantum network control

Innovation

• Decentralized quantum network control via feedback coordination
• QPrimal-Dual algorithm solving Quantum Network Utility Maximization
• Local controllers operating with minimal classical message exchange