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      Quantum adiabatic machine learning

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          Abstract

          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.

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          Most cited references28

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          A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem

          , , (2001)
          A quantum system will stay near its instantaneous ground state if the Hamiltonian that governs its evolution varies slowly enough. This quantum adiabatic behavior is the basis of a new class of algorithms for quantum computing. We test one such algorithm by applying it to randomly generated, hard, instances of an NP-complete problem. For the small examples that we can simulate, the quantum adiabatic algorithm works well, and provides evidence that quantum computers (if large ones can be built) may be able to outperform ordinary computers on hard sets of instances of NP-complete problems.
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            Arcing classifier (with discussion and a rejoinder by the author)

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              Occam's Razor

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                Author and article information

                Journal
                01 September 2011
                Article
                10.1007/s11128-012-0506-4
                1109.0325
                e8420410-df62-49da-ad04-25e3a13c2a2b

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                21 pages, 9 figures
                quant-ph cs.LG

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