qamlz 0.0.1

Train a Binary Classifier using D-Wave's Quantum Annealers.

QAML-Z

This is a supervised ML algorithm used to train a Binary Classifier on D-Wave's Quantum Annealers. The library has been set up to be compatible with Scikit-Learn's data representation.

Installation

Run the following to install: '''python pip install qamlz '''

Contributors

Special thanks to everyone who helped me develop this module: - My PI and hia grascious Grad student - Javier Duarte and Raghav Kansal (University of California San Diego, La Jolla, CA 92093, USA) - All of QMLQCF, with special mentions of: - Jean-Roch (California Institute of Technology, Pasadena, CA 91125, USA) - Daniel Lidar (University of Southern California, Los Angeles, CA 90007, USA) - Gabriel Perdue (Fermi National Accelerator Laboratory, Batavia, IL 60510, USA) - The author of the code this model was built around: - Alexander Zlokapa (Massachusetts Institute of Technology, Cambridge, MA 02139, USA)

Usage

'''python import qamlz

Generate the Environment (Data) for the Model

env = qamlz.TrainEnv(X_train, y_train, endpoint_url, account_token, [X_val, y_val, fidelity])

Generate the Config (Hyperparameters) for the Model

config = qamlz.ModelConfig()

Generate the Model and Begin Training

model = qamlz.ModelConfig(config, env) model.train() '''

Developing Hola Amigos

To install qamlz, along with the tools you need to develop and run tests, run the following in your virtualenv: '''bash $ pip install -e .[dev] '''