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

TL;DR

This work addresses energy-efficient routing in large-scale Wireless Sensor Networks by partitioning the network with spectral clustering and solving intra-cluster routing using distributed QAOA on resource-constrained QPUs within a QUBO framework. It combines a directed graph energy model, a QUBO encoding of routing constraints, and a resource-aware deployment of QAOA across subgraphs, capped by a maximum subgraph size $n_{\max}$. Experiments on a 109-node network show quantum-assisted optimization achieving up to $83.16\%$ energy reduction, outperforming a classical greedy baseline of $68.32\%$. The study demonstrates a scalable pathway to integrate quantum optimization into wireless networks, balancing hardware limits with classical coordination to boost energy efficiency.

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

Figures (4)

• Figure 1: Conceptual overview of the hybrid classical-quantum approach for optimizing WSN routing. The network is partitioned into clusters, each treated as a subgraph optimized by QPUs using the QAOA. The quantum circuit solves intra-cluster routing, while classical methods handle inter-cluster routing. The network comprises base stations (BS, red), cluster heads (CH, green), and cluster members (CM, blue). Detailed optimization methods and routing strategies are further explained later in the paper.
• Figure 2: . Compilation strategies for resource-efficient distributed QAOA. (a) Resource-efficient compilation strategy for a single-core heavy-hexagonal QPUgambetta2020ibm. (b) Cluster-based compilation strategy, where four distinct clusters are compiled and connected to a shared QPU. (c) Modular distributed compilation strategy, involving four independent QPUs connected through modular connectors, supporting efficient inter-cluster communication and execution.
• Figure 3: Initial Wireless Sensor Network Topology with Clusters Highlighted.
• Figure 4: Optimized Routing Paths by Hybrid QAOA Approach.