Everything About ___
Live briefing

Everything about Quadratic unconstrained binary optimization

Combinatorial optimization problem

Quadratic unconstrained binary optimization (QUBO), also known as unconstrained binary quadratic programming (UBQP), is a combinatorial optimization problem with a wide range of applications from finance and economics to machine learning. QUBO is an NP hard problem, and for many classical problems from theoretical computer science, like maximum cut, graph coloring and the partition problem, embeddings into QUBO have been formulated. Embeddings for machine learning models include support-vector machines, clustering and probabilistic graphical models. Moreover, due to its close connection to Ising models, QUBO constitutes a central problem class for adiabatic quantum computation, where it is solved through a physical process called quantum annealing. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Quadratic unconstrained binary optimizationA 5-minute orientation
  2. Skim a recent paper: Classifying and Benchmarking Quantum Annealing Algorithms Based on Quadratic Unconstrained Binary Optimization for Solving NP-Hard ProblemsInfluential work since 2022
  3. Pick up a classic book: Quadratic Unconstrained Binary Optimization ProblemThe most-reprinted book on the subject
  4. Read the landmark paper: Multistart Tabu Search Strategies for the Unconstrained Binary Quadratic Optimization ProblemCited 124 times
  5. Go sideways: Rock mass plasticityA closely related topic
  6. Go sideways: Kernel embedding of distributionsA closely related topic

📖 Definitions

No dictionary entry for this exact phrase.

🗂️ The subject at a glance

  1. Definition
  2. Properties
  3. Applications
  4. Connection to Ising models

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

🔗 Related topics