In our open source library, you are guided through our use-case driven code base through elaborate documentation and some illustrative examples. All software components are self-contained and fully functional, enabling them to be used directly as building blocks for further development of quantum applications. As of now, the library mainly contains packages related to optimisation and machine learning. However, as the library keeps on expanding, so will the horizon of applications.
Our open-source software packages can be found on GitHub and PyPi. The commonly used license by TNO is the Apache License, Version 2.0. This allows for easy adoption and flexible usage without enforcing a specific license to (end-)users and contributors of the codebase.
Domain packages
The Toolbox includes packages related to the beforementioned domains.
Optimisation
When translated into a Quadratic Unconstrained Binary Optimisation (QUBO) problem, any optimisation problem can be run on a quantum annealer, a type of analogue quantum computer. Our optimisation package provides a comprehensive suite of tools for defining QUBO solvers and using them to solve QUBO problems. It furthermore includes features for pre- and post-processing, as well as the ability to create pipelines that integrate all components seamlessly.
Machine learning
Our machine learning package provides a suite of quantum and quantum-inspired tools for computational problems in machine learning. It includes, among others, variational quantum classifiers, quantum support vector machines, a quantum-inspired algorithm for linear regression and quantum clustering algorithms such as K-Mediods and BK-Means. All components are modular and scikit-learn-compatible, enabling experimentation with hybrid quantum-classical workflows.
Get in touch
We are always open to questions about, suggestions for, and contributions to our codebase. Please contact us if you need a commercial license to one or more of our packages.