Quantum Portfolio Optimization
Applying quantum optimization algorithms to financial portfolio selection, aiming to find optimal asset allocations faster than classical solvers.[1]
- Industry: Finance
- Category: finance
- portfolio-optimization
- QAOA
- finance
- hybrid
What is the problem?
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.
How does quantum computing help?
QAOA and variational quantum algorithms encode portfolio constraints into quantum circuits. Hybrid loops optimize parameters classically while evaluating the objective function on quantum hardware.
What are the results?
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.
Frequently Asked Questions
What problem does Quantum Portfolio Optimization solve?
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.
How does quantum computing help?
QAOA and variational quantum algorithms encode portfolio constraints into quantum circuits. Hybrid loops optimize parameters classically while evaluating the objective function on quantum hardware.