ProvideQ aims to enable the Enablers by providing Quantum Readiness for Optimization Providers. We collect quantum and classical algorithms for well-known optimization problems and implement solution strategies to decide which algorithms can provide the best solutions for a specific problem instance. This website represents the current prototype of our toolbox. It is currently in active development.

Feel free to try it out!

Our GitHub: @ProvideQ

API documentation: OpenAPI definition

Contact: provideq@lists.kit.edu

simulated

For a given Boolean formula, this algorithm checks if there exists an interpretation that satisfies it.

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QAOAsimulated

For a given undirected, weighted graph, this algorithm finds a cut that is a maximum in some way or another.

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sub-routines

For a given feature model, check for Void Feature Model, Dead Features, False-Optional Features, Redundant Constraints..

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