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Adiabatic Quantum Computing for Binary Clustering

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arxiv 1706.05528 v1 pith:CVY3OHEV submitted 2017-06-17 stat.ML quant-ph

Adiabatic Quantum Computing for Binary Clustering

classification stat.ML quant-ph
keywords computingquantumadiabaticbinaryclusteringadiabaticallyadoptapproach
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Quantum computing for machine learning attracts increasing attention and recent technological developments suggest that especially adiabatic quantum computing may soon be of practical interest. In this paper, we therefore consider this paradigm and discuss how to adopt it to the problem of binary clustering. Numerical simulations demonstrate the feasibility of our approach and illustrate how systems of qubits adiabatically evolve towards a solution.

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