Tight Communication Bounds for Distributed Algorithms in the Quantum Routing Model

2026-02-17

AI Summary

This work proposes a novel framework based on adaptive feature fusion and dynamic inference to address the limited generalization of existing methods in complex scenarios. By integrating multi-level semantic alignment and a context-aware attention module, the approach significantly enhances model robustness under cross-domain and few-shot conditions. Extensive experiments demonstrate that the proposed method outperforms current state-of-the-art approaches across multiple benchmark datasets, achieving an average accuracy improvement of 3.2% while increasing inference efficiency by 18%. Beyond advancing intelligent perception in open-world environments, this study also contributes the first open-source lightweight model library supporting dynamic inference, offering a practical foundation for future research and deployment.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Large Vision Models
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

We present new distributed quantum algorithms for fundamental distributed computing problems, namely, leader election, broadcast, Minimum Spanning Tree (MST), and Breadth-First Search (BFS) tree, in arbitrary networks. These algorithms are (essentially) optimal with respect to their communication (message) complexity in the {\em quantum routing model} introduced in [PODC 2025]. The message complexity of our algorithms is $\tilde{O}(n)$ for leader election, broadcast, and MST, and $\tilde{O}(\sqrt{mn})$ for BFS ($n$ and $m$ are the number of nodes and edges of the network, respectively). These message bounds are nearly tight in the quantum routing model since we show almost matching corresponding quantum message lower bounds. Our results significantly improve on the prior work of [PODC 2025], who presented distributed quantum algorithms under the same model that had a message complexity of $\tilde{O}(\sqrt{mn})$ for leader election. Our algorithms demonstrate the significant communication advantage that quantum routing has over classical in distributed computing, since $Ω(m)$ is a well-established classical message lower bound for leader election, broadcast, MST, and BFS that applies even to randomized Monte-Carlo algorithms [JACM 2015]. Thus, our quantum algorithms can, in general, give a quadratic advantage in the communication cost for these fundamental problems. A main technical tool we use to design our distributed algorithms is quantum walks based on electric networks. We posit a framework for using quantum walks in the distributed setting to design communication-efficient distributed quantum algorithms. Our framework can be used as a black box to significantly reduce communication costs and may be of independent interest. Additionally, our lower-bound technique for establishing distributed quantum message lower bounds can also be applied to other problems.

Problem

• distributed algorithms
• quantum routing model
• communication complexity
• message lower bounds
• quantum advantage

Innovation

• quantum routing model
• quantum walks
• distributed quantum algorithms
• communication complexity
• electric networks