@Article{jqc.2025.068127,
AUTHOR = {James Hancock, Matthew Craven, Craig McNeile, Davide Vadacchino},
TITLE = {Investigating Techniques to Optimise the Layout of Turbines in a Windfarm Using a Quantum Computer},
JOURNAL = {Journal of Quantum Computing},
VOLUME = {7},
YEAR = {2025},
NUMBER = {1},
PAGES = {55--79},
URL = {http://www.techscience.com/jqc/v7n1/63345},
ISSN = {2579-0145},
ABSTRACT = {This paper investigates Windfarm Layout Optimization (WFLO), where we formulate turbine placement considering wake effects as a Quadratic Unconstrained Binary Optimization (QUBO) problem. Wind energy plays a critical role in the transition toward sustainable power systems, but the optimal placement of turbines remains a challenging combinatorial problem due to complex wake interactions. With recent advances in quantum computing, there is growing interest in exploring whether hybrid quantum-classical methods can provide advantages for such computationally intensive tasks. We investigate solving the resulting QUBO problem using the Variational Quantum Eigensolver (VQE) implemented on Qiskit’s quantum computer simulator, employing a quantum noise-free, gate-based circuit model. Three classical optimizers are discussed, with a detailed analysis of the two most effective approaches: Constrained Optimization BY Linear Approximation (COBYLA) and Bayesian Optimization (BO). We compare these simulated quantum results with two established classical optimization methods: Simulated Annealing (SA) and the Gurobi solver. The study focuses on 4 × 4 grid configurations (requiring 16 qubits), providing insights into near-term quantum algorithm applicability for renewable energy optimization.},
DOI = {10.32604/jqc.2025.068127}
}