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Quantum Optimization
Quantum Machine Learning
Portfolio Optimisation Using the D-Wave Quantum Annealer
arXiv
Authors: Frank Phillipson, Harshil Singh Bhatia
Year
2020
Paper ID
19307
Status
Preprint
Abstract Read
~2 min
Abstract Words
124
Citations
N/A
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.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2020 reference point for readers tracking recent quantum research.
- The first quantum computers are expected to perform well at quadratic optimisation problems.
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