A Tutorial on Formulating and Using QUBO Models

TL;DR

This tutorial explains the QUBO model's fundamentals, its versatility in representing various optimization problems, and recent innovations in solving QUBO models, emphasizing its significance in quantum and classical computing.

Contribution

It provides a comprehensive overview of QUBO formulation, including how to incorporate constraints naturally and recent solution methods, advancing understanding and application in optimization and quantum computing.

Findings

QUBO models can naturally encode constraints using penalty functions.

Recent solution methods enhance classical and quantum optimization.

QUBO models are central to quantum annealing and neuromorphic computing.

Abstract

The Quadratic Unconstrained Binary Optimization (QUBO) model has gained prominence in recent years with the discovery that it unifies a rich variety of combinatorial optimization problems. By its association with the Ising problem in physics, the QUBO model has emerged as an underpinning of the quantum computing area known as quantum annealing and has become a subject of study in neuromorphic computing. Through these connections, QUBO models lie at the heart of experimentation carried out with quantum computers developed by D-Wave Systems and neuromorphic computers developed by IBM. Computational experience is being amassed by both the classical and the quantum computing communities that highlights not only the potential of the QUBO model but also its effectiveness as an alternative to traditional modeling and solution methodologies. This tutorial discloses the basic features of the QUBO model that give it the power and flexibility to encompass the range of applications that have thrust it onto center stage of the optimization field. We show how many different types of constraining relationships arising in practice can be embodied within the "unconstrained" QUBO formulation in a very natural manner using penalty functions, yielding exact model representations in contrast to the approximate representations produced by customary uses of penalty functions. Each step of generating such models is illustrated in detail by simple numerical examples, to highlight the convenience of using QUBO models in numerous settings. We also describe recent innovations for solving QUBO models that offer a fertile avenue for integrating classical and quantum computing and for applying these models in machine learning.