← Back to papers
How to Approximate any Objective Function via Quadratic Unconstrained Binary Optimization
Thomas Gabor, M. Rosenfeld, Sebastian Feld, Claudia Linnhoff-Popien·March 1, 2022·DOI: 10.1109/saner53432.2022.00149
PhysicsComputer Science
AI Breakdown
Get a structured breakdown of this paper — what it's about, the core idea, and key takeaways for the field.
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).