Quantum adiabatic machine learning

Details Quantum adiabatic machine learning Quantum adiabatic machine learning

2011-09-01

arxiv.org/abs/1109.0325v1

Physics

Quantum Physics

21 pages, 9 figures

Scientific paper

We develop an approach to machine learning and anomaly detection via quantum adiabatic evolution. 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. We apply and illustrate this approach in detail to the problem of software verification and validation.

Affiliated with

Also associated with

No associations

Rating

Quantum adiabatic machine learning does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Quantum adiabatic machine learning, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Quantum adiabatic machine learning will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-687262