We test whether quantum-inspired optimization, specifically Quadratic Unconstrained Binary Optimization (QUBO) solved via simulated annealing, can improve implementable portfolio performance compared to classical baselines when realistic frictions are included. Our experimental design uses daily returns from the Center for Research in Security Prices (CRSP) for S vs. mean–variance: −0.29, 95% CI −0.63,0.06). Excessive turnover (averaging ∼1.0 per rebalance) is identified as the primary bottleneck. We conclude that current QUBO formulations with simulated annealing do not yet deliver a robust, implementable edge over well-tuned convex optimizers, though the framework remains a promising research direction as quantum hardware and hybrid solvers mature.
Ekow Tawiah Andoh (Tue,) studied this question.