Quantum Portfolio Optimization
Finance · Finance
Applying quantum optimization algorithms to financial portfolio selection, aiming to find optimal asset allocations faster than classical solvers.
Applying quantum optimization algorithms to financial portfolio selection, aiming to find optimal asset allocations faster than classical solvers.
Classical portfolio optimization becomes computationally intractable as the number of assets, constraints, and risk factors grows. The problem is NP-hard in its general form.
QAOA and variational quantum algorithms encode portfolio constraints into quantum circuits. Hybrid loops optimize parameters classically while evaluating the objective function on quantum hardware.
Demonstrations on small portfolios (10-50 assets) show competitive results with classical methods. Quantum advantage for real-world portfolio sizes is expected as hardware scales.