Quantum Information Processing

, Volume 12, Issue 5, pp 2027–2070 | Cite as

Quantum adiabatic machine learning

  • Kristen L. PudenzEmail author
  • Daniel A. Lidar


We develop an approach to machine learning and anomaly detection via quantum adiabatic evolution. This approach consists of two quantum phases, with some amount of classical preprocessing to set up the quantum problems. In the training phase we identify an optimal set of weak classifiers, to form a single strong classifier. In the testing phase we adiabatically evolve one or more strong classifiers on a superposition of inputs in order to find certain anomalous elements in the classification space. Both the training and testing phases are executed via quantum adiabatic evolution. All quantum processing is strictly limited to two-qubit interactions so as to ensure physical feasibility. We apply and illustrate this approach in detail to the problem of software verification and validation, with a specific example of the learning phase applied to a problem of interest in flight control systems. Beyond this example, the algorithm can be used to attack a broad class of anomaly detection problems.


Adiabatic quantum computation Quantum algorithms  Software verification and validation Anomaly detection 



The authors are grateful to the Lockheed Martin Corporation for financial support under the URI program. KP is also supported by the NSF under a graduate research fellowship. DAL acknowledges support from the NASA Ames Research Center.


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Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Department of Electrical Engineering, Center for Quantum Information Science and TechnologyUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Departments of Electrical Engineering, Chemistry, and Physics, Center for Quantum Information Science and TechnologyUniversity of Southern CaliforniaLos AngelesUSA

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