Exploring-Quantum-Portfolio-Optimization

In this project, I study the portfolio optimization problem using both gate-based (VQE) and quantum annealing approaches. Starting with a 0–1 mean-variance model under integer budget constraints, it extends to a discrete-weight mean-variance model, a discretized version of the original problem as proposed by Harry Markowitz. I use brute-force methods to obtain classical baselines, and use them to compare the performance of the quantum solvers. This study demonstrates how quantum optimization techniques can be used to address the combinatorial complexity of portfolio selection.