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Training a Binary Classifier with the Quantum Adiabatic Algorithm · Around

2008

Training a Binary Classifier with the Quantum Adiabatic Algorithm

Neven, Hartmut, Denchev, Vasil S., Rose, Geordie et al.

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This paper describes how to make the problem of binary classification amenable to quantum computing.

A formulation is employed in which the binary classifier is constructed as a thresholded linear superposition of a set of weak classifiers.
The weights in the superposition are optimized in a learning process that strives to minimize the training error as well as the number of weak classifiers used.
No efficient solution to this problem is known.

Built on

On the density of families of sets
Norbert Sauer · 1972
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Arrangements of hyperplanes
Peter Orlik and Hiroaki Terao · 1992
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Solving quadratic (0,1)-problems by semidefinite programs and cutting planes
Christoph Helmberg and Franz Rendl · 1998
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A short introduction to boosting
Yoav Freund and Robert E. Schapire · 1999
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