Energy Internet Routing using Quantum Optimization Algorithms
Abstract
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.
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BibTeX
@misc{tehrani2026energy,
title = {{Energy Internet Routing using Quantum Optimization Algorithms}},
author = {Alireza Alamgir Tehrani and Mehrdad Boroushaki and Abbas Rajabi},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2608.28401},
eprint = {2608.28401},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.28401},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - Energy Internet Routing using Quantum Optimization Algorithms
AU - Alireza Alamgir Tehrani
AU - Mehrdad Boroushaki
AU - Abbas Rajabi
PY - 2026
DA - 2026/08/28
PB - arXiv
DO - 10.48550/arxiv.2608.28401
UR - https://arxiv.org/abs/2608.28401
AB - 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.
ER -