qamlz 0.0.2

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:

$ pip install qamlz

Contributors

Special thanks to everyone who helped me develop this module:

• My PI and 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)
• Mentoring for code practices:
  • Otto Sievert (GoPro, Inc.)

Usage

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 QAML-Z

To install qamlz, along with the tools you need to develop and run tests, run the following in your virtualenv:

$ pip install -e .[dev]