Can quantum computing surpass classical algorithms in optimizing building performance? A benchmark study with 15,000 simulations

• Quantum and classical algorithms are benchmarked for building performance optimization. • A surrogate model is trained on 15,000 IDA ICE simulations for prediction. • NSGA-II achieves the best trade-offs between energy use and thermal comfort. • Quantum methods deliver faster results but with reduced solution quality. • Quantum computing shows potential for future real-time building optimization. Optimizing building performance is essential for enhancing energy efficiency and occupant comfort. This study evaluates the applicability of quantum computing–based optimization methods in the Architecture, Engineering, and Construction (AEC) industry by comparing the Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing (QA) with classical multi-objective optimization algorithms, namely Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). A dataset of 15,000 building simulations was used to train an Extreme Gradient Boosting (XGBoost) model for predicting total energy consumption (kWh/m 2 /year) and Predicted Percentage of Dissatisfied (PPD) occupants. These predictions were then used in the optimization phase. NSGA-II produced the best trade-offs, achieving energy consumption between 17.84 and 19.84 kWh/m 2 /year and PPD below 5.2 %, with strong diversity and convergence. QAOA executed faster (0.54 min) than NSGA-II (18.9 min) but resulted in higher energy values (31.85–55.62 kWh/m 2 /year) and weaker solution quality. Quantum Annealing completed in 0.37 min but returned clustered solutions near 45.88 kWh/m 2 /year. While the current limitations of quantum methods constrain their effectiveness, the findings indicate their potential as fast solvers in future building performance optimization workflows, particularly as hardware and algorithmic capabilities mature.