IBM and Vanguard researchers demonstrated a quantum-classical workflow for portfolio construction using 109 qubits on IBM’s Heron processors, showing potential advantages for large-scale financial optimization. By combining quantum circuits that explore high-dimensional solution spaces with classical algorithms that refine and validate results, researchers can tackle problems that are too large or too complex for either quantum or classical methods alone. The team applied a Conditional Value at Risk-based Variational Quantum Algorithm (CVaR-VQA), combining quantum sampling and classical optimization to balance asset selection under risk and constraint conditions. According to the study, the team compared a standard TwoLocal circuit with a more advanced design called bias-field counterdiabatic optimization, or BFCD. Early simulations suggested that the harder-to-simulate BFCD circuits produced better convergence. This result hints at a possible sweet spot: quantum circuits that are too complex for efficient classical emulation but still trainable on hardware may deliver the most useful outcomes. The experiments also tested different entanglement structures, including bilinear chains and “colored” maps tailored to IBM’s hexagon-based design, or heavy-hex topology. The study argues that a quantum-classical workflow provides benefits beyond raw accuracy. Because the sampling-based method does not require rewriting the portfolio problem into strict mathematical forms like QUBOs, it preserves more realism in financial modeling. The approach also yields multiple candidate solutions along the way, offering investors richer data for decision-making. At the same time, the hardware results demonstrate that convergence continues even under noise, showing robustness of the method. For finance, the experiments show a path to exploring bond or ETF construction with greater flexibility and possibly faster turnaround in the future. For quantum computing, they provide evidence that harder-to-simulate circuits may be the most promising candidates for practical advantage. The results also suggest new benchmarking possibilities: using realistic financial optimization tasks rather than abstract problems as yardsticks for quantum progress.