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{{unreferenced|date=December 2008}}
'''Quadratic unconstrained binary optimization''' ('''QUBO''') is a [[pattern matching]] technique, common in [[machine learning]] applications. QUBO is an [[NP hard]] problem. 

QUBO problems are particularly well suited for processing on [[Quantum computing|quantum computers]].<ref>{{cite web|url=http://www.technologyreview.com/view/514686/d-waves-quantum-computer-goes-to-the-races-wins/
| title      = D-Wave’s Quantum Computer Goes to the Races, Wins
| author     = Tom Simonite
| publisher  = MIT Technology Review
| date       = {{date|May 8, 2013}}
| accessdate = {{date|12 may 2013}}
}}</ref> 

QUBO is given by the formula:
<math>E(X_1, X_2, ... , X_N) = \sum_{i<j=1}^N Q_{ij} \times X_i \times X_j</math>

==References==

{{Reflist}}

==External links==
* {{cite journal
| url        = http://portal.acm.org/citation.cfm?id=1231283
| title      = Local search heuristics for Quadratic Unconstrained Binary Optimization (QUBO)
| journal    = Journal of Heuristics
| doi        = 10.1007/s10732-007-9009-3
| author     = Endre Boros, Peter L Hammer & Gabriel Tavares
| volume     = Vol 13 Issue 2
| date       = April 2007
| pages      = 99–132
| publisher  = Association for Computing Machinery
| accessdate = {{date|12 may 2013}}
}}
* {{cite journal
| url        = http://www.sciencedirect.com/science/article/pii/S0166218X09003059
| title      = Analyzing quadratic unconstrained binary optimization problems via multicommodity flows
| journal    = Discrete Applied Mathematics
| author     = Di Wang & Robert Kleinberg
| volume     = Vol 157 Issue 18
| date       = November 2009
| doi        = 10.1016/j.dam.2009.07.009
| pages      = 3746–3753
| publisher  = Elsevier
| accessdate = {{date|12 may 2013}}
}}

[[Category:Machine learning algorithms]]


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