Jump to content

Quadratic unconstrained binary optimization

From Wikipedia, the free encyclopedia
This is an old revision of this page, as edited by Citation bot (talk | contribs) at 04:31, 21 September 2020 (Add: s2cid. | You can use this bot yourself. Report bugs here. | Suggested by Abductive | Category:Machine learning algorithms | via #UCB_Category). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

Quadratic unconstrained binary optimization (QUBO) is a pattern matching technique, common in machine learning applications. QUBO is an NP hard problem. Examples of problems that can be formulated as QUBO problems are the Maximum cut, Graph coloring and the Partition problem.[1]

QUBO problems may sometimes be well-suited to algorithms aided by quantum annealing.[2]

QUBO is the problem of minimizing a quadratic polynomial over binary variables. The quadratic polynomial will be of the form with and .

References

  1. ^ Glover, Fred; Kochenberger, Gary (2019). "A Tutorial on Formulating and Using QUBO Models". arXiv:1811.11538 [cs.DS].
  2. ^ Tom Simonite (8 May 2013). "D-Wave's Quantum Computer Goes to the Races, Wins". MIT Technology Review. Retrieved 12 May 2013.