Portfolio Asset Identification Using Graph Algorithms on a Quantum Annealer

Abstract

The dual objectives of this paper are to explore how commercially available quantum hardware and algorithms can solve real-world problems in finance, and then compare quantum solutions to their classical counterparts. Specifically, the D-Wave quantum annealing computer (D-Wave 2000Q) is used to address the problem of asset correlation identification for financial portfolio management. Graphical models offer a natural framework to represent asset correlations. Graphs also naturally map the quantum annealing hardware architecture developed by the D-Wave. The paper explores how graph algorithms can be implemented on the D-Wave 2000Q machine to cluster asset correlations in order to identify various financial portfolios. Numerical experiments are conducted using four quantum/classical algorithm pairs on four real world financial time series datasets spanning 10 years. For the specific algorithms and datasets selected, the quantum solution is competitive with (and sometimes better than) the classical one. However, quantum fails to scale beyond certain levels of data dimensionality. The study focuses on comparison of solution quality not speedup, and the results suggest specific high-potential directions for future research.