Quantum adiabatic machine learning

@article{Pudenz2013QuantumAM,
  title={Quantum adiabatic machine learning},
  author={Kristen L. Pudenz and Daniel A. Lidar},
  journal={Quantum Information Processing},
  year={2013},
  volume={12},
  pages={2027-2070}
}
  • Kristen L. Pudenz, Daniel A. Lidar
  • Published in
    Quantum Information…
    2013
  • Physics, Computer Science
  • Abstract 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… CONTINUE READING

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