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Solve a binary classification problem with Qboost

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Qboost

The D-Wave quantum computer has been widely studied as a discrete optimization engine that accepts any problem formulated as quadratic unconstrained binary optimization (QUBO). In 2008, Google and D-Wave published a paper, Training a Binary Classifier with the Quantum Adiabatic Algorithm, which describes how the Qboost ensemble method makes binary classification amenable to quantum computing: the problem is formulated as a thresholded linear superposition of a set of weak classifiers and the D-Wave quantum computer is used to optimize the weights in a learning process that strives to minimize the training error and number of weak classifiers

This code demonstrates the use of the D-Wave system to solve a binary classification problem using the Qboost algorithm.

Disclaimer

This demo and its code are intended for demonstrative purposes only and are not designed for performance.

Usage

A minimal working example using the main interface function can be seen by running:

python demo.py  --wisc --mnist

References

H. Neven, V. S. Denchev, G. Rose, and W. G. Macready, "Training a Binary Classifier with the Quantum Adiabatic Algorithm", arXiv:0811.0416v1

License

Released under the Apache License 2.0. See LICENSE file.

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