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Quantum Optimization Quantum Machine Learning

Portfolio Optimization of 60 Stocks Using Classical and Quantum Algorithms

arXiv

Authors: Jeffrey Cohen, Alex Khan, Clark Alexander

Year

2020

Paper ID

21344

Status

Preprint

Abstract Read

~2 min

Abstract Words

100

Citations

N/A

Abstract

We continue to investigate the use of quantum computers for building an optimal portfolio out of a universe of 60 U.S. listed, liquid equities. Starting from historical market data, we apply our unique problem formulation on the D-Wave Systems Inc. D-Wave 2000Q (TM) quantum annealing system (hereafter called D-Wave) to find the optimal risk vs return portfolio. We approach this first classically, then using the D-Wave, to select efficient buy and hold portfolios. Our results show that practitioners can use either classical or quantum annealing methods to select attractive portfolios. This builds upon our prior work on optimization of 40 stocks.

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.
We continue to investigate the use of quantum computers for building an optimal portfolio out of a universe of 60 U.S.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2008.08669 [2] arXiv https://arxiv.org/abs/2008.08669 [3] Publisher https://arxiv.org/abs/2008.08669

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