Resource-Efficient Compilation of Distributed Quantum Circuits for Solving Large-Scale Wireless Communication Network Problems

TL;DR

This paper presents a resource-efficient method for compiling distributed quantum circuits to optimize large-scale wireless sensor network routing, combining classical and quantum techniques for improved scalability and energy efficiency.

Contribution

It introduces a hybrid classical-quantum framework using spectral clustering and QAOA for scalable network routing optimization in wireless sensor networks.

Findings

Significant energy savings compared to classical algorithms

Enhanced scalability for large network routing problems

Effective integration of quantum algorithms into network optimization

Abstract

Optimizing routing in Wireless Sensor Networks (WSNs) is pivotal for minimizing energy consumption and extending network lifetime. This paper introduces a resourceefficient compilation method for distributed quantum circuits tailored to address large-scale WSN routing problems. Leveraging a hybrid classical-quantum framework, we employ spectral clustering for network partitioning and the Quantum Approximate Optimization Algorithm (QAOA) for optimizing routing within manageable subgraphs. We formulate the routing problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem, providing comprehensive mathematical formulations and complexity analyses. Comparative evaluations against traditional classical algorithms demonstrate significant energy savings and enhanced scalability. Our approach underscores the potential of integrating quantum computing techniques into wireless communication networks, offering a scalable and efficient solution for future network optimization challenges