July 1, 2020 update:
We plan to publish our article on arXiv later this week. Should be a good, practical and academic article relevant to financial services, stock market investors, and quantum annealing users.
Current title draft:
Demonstrating Quantum Advantage in Portfolio Optimization Using DWave’s Quantum Annealer
Jeffrey Cohen, Alex Khan and Clark Alexander
We optimize a portfolio classically & with a DWave Systems annealing quantum computer. As we push the quantum annealer to do more (affine transformations and tuning), we apply new classical methods (and the race continues).
We investigate the use of quantum computers for the building a portfolio out of a universe of U.S. listed, liquid equities that contains an optimal set of stocks. Starting from historical market data, we look at various problem formulations on the DWave annealing quantum computer to find the minimum risk portfolio, an optimized portfolio based on the Markowitz formulation and the Sharpe Ratio, a simplified Chicago Quantum Ratio (CQR), then a new Chicago Quantum Net Score (CQNS). We approach this problem in multiple ways; solving it classically, then by our method on an annealing quantum computer at the current stage of maturity. Our results show that practitioners can use a DWave annealing quantum computer to select attractive portfolios out of 40 U.S. liquid equities.
We recently added a genetic algorithm that provides one portfolio out of 1.1T in ~30 seconds, and plan to add a simulated annealer to the classical mix. On the other hand, we are using a DWave computer model (2000Q) first released in 2017.
Please watch a brief preview below:
Comments and questions welcome!
Strategic IT Management Consultant with a strong interest in Quantum Computing. Consulting for 29 years and this looks as interesting as cloud computing was in 2010.