WARNING: This repository is obsolete. The Qboost demo is now maintained in dwave-examples/qboost.

Demo of 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.

For state-of-the-art machine learning, please contact Quadrant, the machine learning business unit of D-Wave Systems.

Setting Up the Demo

It's recommended that you work in a virtual environment on your local machine; depending on your machine, you may need to first install virtualenv and then create a virtual environment, for example:

virtualenv env
cd env
source ./bin/activate

Copy (clone) this Qboost repository to your local machine's newly created virtual environment.

To set up the required dependencies, in the root directory of a copy (clone) of this repository, run the following:

pip install -r requirements.txt

Configuring the Demo

Access to a D-Wave system must be configured, as described in the dwave-cloud-client documentation. A default solver is required.

Running the Demo

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

python demo.py  --wisc --mnist

License

Released under the Apache License 2.0. See LICENSE file.