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Quantum Algorithms for Mixed Binary Optimization applied to Transaction Settlement

arXiv
Authors: Lee Braine, Daniel J. Egger, Jennifer Glick, Stefan Woerner

Year

2019

Paper ID

7243

Status

Preprint

Abstract Read

~2 min

Abstract Words

97

Citations

N/A

Abstract

We extend variational quantum optimization algorithms for Quadratic Unconstrained Binary Optimization problems to the class of Mixed Binary Optimization problems. This allows us to combine binary decision variables with continuous decision variables, which, for instance, enables the modeling of inequality constraints via slack variables. We propose two heuristics and introduce the Transaction Settlement problem to demonstrate them. Transaction Settlement is defined as the exchange of securities and cash between parties and is crucial to financial market infrastructure. We test our algorithms using classical simulation as well as real quantum devices provided by the IBM Quantum Computation Center.

Why This Paper Matters

  • This paper contributes to the Quantum Simulation research area in the Quantum Articles archive.
  • It adds a 2019 reference point for readers tracking recent quantum research.
  • We extend variational quantum optimization algorithms for Quadratic Unconstrained Binary Optimization problems to the class of Mixed Binary Optimization problems.

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