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.
This demo and its code are intended for demonstrative purposes only and are not designed for performance.
A minimal working example using the main interface function can be seen by running:
python demo.py --wisc --mnistH. Neven, V. S. Denchev, G. Rose, and W. G. Macready, "Training a Binary Classifier with the Quantum Adiabatic Algorithm", arXiv:0811.0416v1
Released under the Apache License 2.0. See LICENSE file.