Quantum Computing for Portfolio Optimization

In collaboration with Vanguard, this project explores the application of quantum computing techniques to portfolio optimization problems in financial markets. Portfolio construction is typically formulated as a constrained optimization task, where investors must balance risk, return, and diversification while operating under real-world trading constraints.

The project investigates sampling-based quantum optimization methods for solving portfolio allocation models formulated with binary decision variables and quadratic objective functions. By modeling realistic trading scenarios including discrete asset selection and risk–return trade offs, the study evaluates whether quantum optimization approaches can offer advantages over conventional techniques used in financial modeling. Particular emphasis is placed on maintaining interpretability, robustness, and consistency with established investment principles, ensuring that algorithmic solutions remain practical for financial decision-making.

The project is currently in progress and aims to assess the viability of quantum-enhanced portfolio optimization workflows for institutional investment use cases, providing insights into how quantum sampling and hybrid quantum-classical optimization strategies could support financial analytics.

WISER Research Fellows: Shiplu Sarker, Nitesh Kansara, Varun Puram