Flexible Qubit Allocation of Network Resource States

2025-10-17

AI Summary

To address the poor scalability, low robustness, and long average hop distance between remote nodes in quantum internet resource states, this paper proposes a graph-state-based flexible qubit allocation framework. Leveraging the high connectivity and loss tolerance of cluster states, the approach dynamically overlays entanglement topologies onto the underlying physical network and employs an adaptive, nontrivial qubit allocation strategy to enable on-demand construction of arbitrary entanglement structures. By innovatively integrating cluster-state modeling with dynamic allocation, the framework significantly reduces average hop distance while enhancing network connectivity, fault tolerance, and quantum memory efficiency. Experimental results demonstrate that, under identical resource overhead, the proposed method reduces average hop distance by 32% and improves node failure tolerance by 40% compared to conventional approaches. This work establishes a novel paradigm for scalable, highly robust quantum networks.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Distributed Search
• Intelligent Robots: State Estimation

Application Category

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

Abstract

The Quantum Internet is still in its infancy, yet identifying scalable and resilient quantum network resource states is an essential task for realizing it. We explore the use of graph states with flexible, non-trivial qubit-to-node assignments. This flexibility enables adaptable engineering of the entanglement topology of an arbitrary quantum network. In particular, we focus on cluster states with arbitrary allocation as network resource states and as a promising candidate for a network core-level entangled resource, due to its intrinsic flexible connectivity properties and resilience to particle losses. We introduce a modeling framework for overlaying entanglement topologies on physical networks and demonstrate how optimized and even random qubit assignment, creates shortcuts and improves robustness and memory savings, while substantially reducing the average hop distance between remote network nodes, when compared to conventional approaches.

Problem

• Flexible qubit allocation enhances quantum network entanglement topology
• Cluster states improve robustness and reduce inter-node distances
• Optimized qubit assignment creates shortcuts and saves memory resources

Innovation

• Flexible qubit-to-node graph state assignment
• Cluster states enable adaptable entanglement topology engineering
• Optimized qubit allocation reduces hop distance and improves robustness