Portfolio Optimisation Using the D-Wave Quantum Annealer

TL;DR

This paper explores the application of D-Wave's quantum annealer to solve portfolio optimisation problems in finance, benchmarking its performance against classical methods on real market data.

Contribution

It demonstrates the feasibility of using quantum annealing for portfolio optimisation and compares its performance with traditional solvers on real-world financial data.

Findings

Quantum annealer solutions are close to classical solvers for small instances.

D-Wave's hybrid solvers show promising results in financial optimisation.

Quantum approaches can be competitive with traditional methods at current hardware sizes.

Abstract

The first quantum computers are expected to perform well at quadratic optimisation problems. In this paper a quadratic problem in finance is taken, the Portfolio Optimisation problem. Here, a set of assets is chosen for investment, such that the total risk is minimised, a minimum return is realised and a budget constraint is met. This problem is solved for several instances in two main indices, the Nikkei225 and the S\&P500 index, using the state-of-the-art implementation of D-Wave's quantum annealer and its hybrid solvers. The results are benchmarked against conventional, state-of-the-art, commercially available tooling. Results show that for problems of the size of the used instances, the D-Wave solution, in its current, still limited size, comes already close to the performance of commercial solvers.