基于量子优化算法的能源互联网路由
Energy Internet Routing using Quantum Optimization Algorithms
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中文总结 AI 辅助
针对能源互联网高效路径选择的NP难问题，提出QUBO与伊辛哈密顿量公式化方法，通过9节点和30节点网络案例验证，仿真结果与精确解一致，经典方法运行时短于量子方法，其中量子方法里Simulated Annealing采样器最快。
中文摘要 AI 辅助
能源互联网（Energy Internet, EI）是旨在提升可再生能源与电网融合度的新概念，其高效路径选择问题属于NP难问题。本研究提出一种创新的二次无约束二元优化（Quadratic Unconstrained Binary Optimization, QUBO）与伊辛哈密顿量（Ising Hamiltonian）公式化方法用于能源路由。通过将量子启发退火和量子门优化应用于9节点与30节点EI网络两个案例研究，评估所提公式化方法的有效性与可扩展性。对比分析采用经典优化方法（Dijkstra算法、基于优化的方法）、量子近似优化算法（Quantum Approximate Optimization Algorithm, QAOA，基于Qiskit Sampler Primitive、NumpyEigenSolver）、量子启发退火（基于Ocean exact Solver、D-Wave Tabu Sampler、D-Wave Simulated Annealing）。基于所提公式化方法的仿真结果与精确解一致，经典方法的运行时短于量子方法，但Simulated Annealing采样器在所有量子方法中运行时最短。
英文摘要
The Energy Internet (EI) is a new concept aimed at enhancing the integration of renewable energy sources with the energy grid. Energy-efficient path selection in EI is NP-hard. This research presents an innovative Quadratic Unconstrained Binary Optimization (QUBO) and Ising Hamiltonian formulation for energy routing. The validation and scalability of the proposed formulation were evaluated by applying quantum-inspired annealing and quantum gate optimization to two case studies, a 9-node and a 30-node EI network. A comparative analysis was presented between classical optimization using the Dijkstra algorithm, optimization-based methods, and QAOA using the Qiskit Sampler Primitive, NumpyEigenSolver, and quantum-inspired annealing using the Ocean exact Solver, D-Wave Tabu Sampler, and D-Wave Simulated Annealing. Simulation results based on the proposed formulation agree with the exact solution, while the runtime of classical approaches is less than that of quantum approaches. However, the Simulated Annealing sampler offers the shortest runtime among all quantum methods.
