Quantum Portfolio Optimization Under Realistic Frictions: A Rigorous Comparison with Classical Baselines

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&P500 constituents spanning 2014–2025, with monthly rebalancing, explicit transaction costs, and turnover accounting. We compare QUBO portfolios against equal-weight and convex mean–variance portfolios across multiple universe sizes (20, 50, 67 assets), lookback windows, and transaction-cost levels. Statistical significance is assessed via paired moving-block bootstrap on out-of-sample net returns. A hyperparameter sweep over encoding depth, penalty coefficients, and turnover reveals 19 of 81 QUBO configurations beat the equal-weight baseline, with the best achieving a Sharpe of 1.59 versus 1.42. However, under a fair walk forward model-selection protocol that eliminates look-ahead bias, the QUBO strategy does not achieve statistically significant superiority over either classical benchmark (∆Sharpe vs. equal weight: −0.13, 95% CI [−0.46,0.16]; 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.