For the past two years, Rahko researchers have developed Hyrax, a framework for developing and deploying quantum machine learning algorithms.
Hyrax uses a proprietary HPC pipeline for the integration of quantum and classical hardware, and is hardware agnostic, integrating with leading quantum computers.
With an easy-to-use high-level Python interface, Hyrax offers multiple pre-made models for quantum classical hybrid computation including parametrized quantum circuits such as the Variational Quantum Eigensolver (VQE) for chemical simulation and the Quantum Generative Adversarial Network (QGAN) for learning to approximate quantum states.
Hyrax continues to grow as we consolidate our work into it, and powers all of our customer and internal research.
We are thrilled to launch the Hyrax website, with a static demo of the framework.
Click here to visit the Hyrax website and static demo.