Designing quantum intelligent algorithms for portfolio optimization problems

Portfolio optimization is pivotal in the financial domain, aiming to allocate assets to maximize returns and minimize risk. Conventional optimization techniques often fall short due to the financial markets’ intricate nature, the optimization landscape’s non-convexity, and the vastness of possible portfolios. This project introduces a novel approach by integrating quantum-inspired methods with machine learning techniques to tackle portfolio optimization problems. The quantum-inspired methods harness the principles of quantum mechanics, specifically superposition and entanglement, to navigate complex optimization landscapes efficiently. Meanwhile, machine learning, particularly deep learning modeling, is utilized to capture and predict intricate market patterns, thus guiding the algorithm toward promising solutions. The amalgamation of these techniques aims to substantially outperform classical portfolio optimization methods in terms of computational efficiency and quality of solutions. Preliminary results demonstrate that our quantum intelligent approach is more adept at identifying optimal and diverse portfolios and holds significant promise for the future of financial analytics and decision-making.

Faculty Supervisor:

Roger Melko

Student:

Partner:

yiyaniQ Inc.

Discipline:

Physics

Sector:

Information and cultural industries

University:

University of Waterloo

Program: