Advancing Portfolio Optimization through Quantum Computing

This research focuses on the application of advanced quantum algorithms, such as the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE), to improve portfolio optimization techniques.

The research demonstrates the potential of quantum computing by benchmarking QAOA’s performance against classical optimization methods for maximizing the Sharpe ratio. Implementations include constructing Hamiltonians in QAOA, where ground states encode optimal portfolio solutions, and a classical optimization approach that calculates weighted portfolios. The results show consistency between quantum and classical methods, as QAOA identifies the same top assets as classical optimization. This consistency highlights quantum computing’s promise as a complementary tool for financial optimization. The implementations for this research, including the QAOA and classical optimization approaches, are available in the GitHub repository quantum-portfolio-optimization.

While the results are promising, certain challenges persist. Optimizing the parameters of the current cost function proved difficult in datasets with closely aligned returns, where covariance plays a significant role. To address these challenges, the research explores refinements to the QUBO formulation to improve interpretability and scalability, enabling more intuitive parameter tuning and practical investment decisions.

Moving forward, this research will focus on exploring various QAOA versions and optimizing its initial hyperparameters. The objective is to identify the most effective approach for optimizing the refined QUBO formulation.