Resource-Efficient Compilation of Distributed Quantum Circuits for Solving Large-Scale Wireless Communication Network Problems
🤖 AI Summary
To address high energy consumption and routing optimization challenges in large-scale wireless sensor networks (WSNs), this paper proposes a distributed quantum-classical collaborative routing optimization framework tailored for WSNs. Methodologically, the network is first partitioned via spectral clustering; each subgraph’s routing problem is then formulated as a Quadratic Unconstrained Binary Optimization (QUBO) instance and solved on quantum hardware using the Quantum Approximate Optimization Algorithm (QAOA); finally, distributed quantum circuit compilation techniques are employed to enhance execution efficiency. Key contributions include: (i) the first distributed quantum compilation architecture specifically designed for WSN resource constraints; (ii) a scalable paradigm integrating classical preprocessing with quantum optimization; and (iii) a complete QUBO modeling framework with rigorous theoretical complexity analysis. Experimental results on thousand-node networks demonstrate an average 23.6% reduction in energy consumption and a 3.8× speedup over conventional algorithms, significantly extending network lifetime.
Application Category
📝 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
Problem
Research questions and friction points this paper is trying to address.
Energy Efficiency
Optimal Pathfinding
Wireless Network
Innovation
Methods, ideas, or system contributions that make the work stand out.
Quantum Computing
Wireless Sensor Networks
Hybrid Computing Techniques
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