WARNING: This repository is obsolete. The Qboost demo is now maintained in dwave-examples/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.
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.
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/activateCopy (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.txtAccess to a D-Wave system must be configured, as described in the dwave-cloud-client documentation. A default solver is required.
A minimal working example using the main interface function can be seen by running:
python demo.py --wisc --mnistReleased under the Apache License 2.0. See LICENSE file.