@inproceedings{04ddcbb5d58b4db6bb780b85a7fde96e,
title = "Portfolio Optimisation Using the D-Wave Quantum Annealer",
abstract = "The first quantum computers are expected to perform well on 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{\textquoteright}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.",
keywords = "quantum portfolio optimisation, quadratic unconstrained binary optimisation, quantum annealing, genetic algorithm",
author = "Frank Phillipson and Bhatia, {Harshil Singh}",
note = "DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.",
year = "2021",
doi = "10.1007/978-3-030-77980-1_4",
language = "English",
isbn = "978-3-030-77979-5",
series = "Lecture Notes in Computer Science",
publisher = "Springer, Cham",
pages = "45--59",
editor = "Maciej Paszynski and Dieter Kranzlm{\"u}ller and Krzhizhanovskaya, {Valeria V.} and Dongarra, {Jack J.} and Sloot, {Peter M.A.}",
booktitle = "Computational Science – ICCS 2021",
address = "Switzerland",
}