Researchers Adopt Reverse Annealing Technique for Portfolio Optimization Experiment
(Risk-Net) Quantum computers are potentially ideal for solving optimization problems. Portfolio optimization is the process of selecting the best mix of assets from all possible combinations, as pioneered by Harry Markowitz, is as old as modern finance, but it is still a notoriously complex problem to solve in its discrete form – assuming that assets are traded in specific units – when the number of underlying assets gets large.
Davide Venturelli, a quantum computing science lead at the USRA Research Institute for Advanced Computer Science in the US, and Alexei Kondratyev, a managing director and head of data analytics in the electronic market solutions team at Standard Chartered Bank in London attempted adopted a quantum computing technique called reverse annealing, using the D-Wave quantum annealer located at NASA’s Ames Research Center to optimize a portfolio of 60 assets. The reverse annealer generates results 100–1,000 times faster than a classical technique if overhead times for running the quantum annealer are not added. Their research lays the foundations for learning how such problems need to be defined for the quantum annealer, which is as important as proving the technology can achieve faster calculations.