How to Approximate any Objective Function via Quadratic Unconstrained Binary Optimization

Conference paper (2022)

Authors

Thomas Gabor Ludwig Maximilians University
Marian Lingsch Rosenfeld Ludwig Maximilians University
Claudia Linnhoff-Popien Ludwig Maximilians University
S. Feld Quantum Circuit Architectures and Technology - EEMCS
Research Group
Quantum Circuit Architectures and Technology (EEMCS) (TU Delft)
Copyright: © 2022 Thomas Gabor, Marian Lingsch Rosenfeld, Claudia Linnhoff-Popien, S. Feld
DOI: https://doi.org/10.1109/SANER53432.2022.00149
Quantum computing QUBO Quadratic unconstrained binary optimization
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Published Date

2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Quantum & Computer Engineering

Research Group

Quantum Circuit Architectures and Technology

Abstract

Quadratic unconstrained binary optimization (QUBO) has become the standard format for optimization using quantum computers, i.e., for both the quantum approximate optimization algorithm (QAOA) and quantum annealing (QA). We present a toolkit of methods to transform almost arbitrary problems to QUBO by (i) approximating them as a polynomial and then (ii) translating any polynomial to QUBO. We showcase the usage of our approaches on two example problems (ratio cut and logistic regression).