Quantum for Energy and Utilities 2026

CASE STUDY 8 Quantum computing Optimizing wind farm layout with VQE hybrid quantum- computing algorithms Turbine performance is affected by changing environmental conditions, such as humidity, temperature, salinity, airflow, turbulence and geographic location, which influence energy density and can cause wear or corrosion over time. Carefully adjusting turbine configurations and layout can reduce wake effects between turbines, maximize power output and extend equipment lifespan by up to 25%, as analysed in a study published in the European Academy of Wind Energy.12 However, evaluating the vast number of possible positioning combinations makes wind farm layout optimization a computationally challenging combinatorial problem. Quantum computing offers a way to accelerate this optimization process, helping balance aerodynamics, structural weight, efficiency and durability more effectively. A test is conducted by formulating the wind farm layout optimization into a QUBO model and applying a hybrid quantum-classical variational quantum eigensolver (VQE) algorithm. Implemented using IBM’s Qiskit gate-based quantum circuit simulator, the approach serves as a proof of concept, demonstrating the viability of the quantum algorithm to find optimal or near-optimal wind layout for small instances.13 Quantum for Energy and Utilities: Key Opportunities for Energy Transition 20