QuaST Decision Tree - Automating quantum-enhanced solutions for optimization

The QuaST Decision Tree framework builds the foundation of an abstraction layer for quantum computing. It allows the automated selection of quantum-enhanced solutions for optimization problems, and enables industrial end users from, e. g., logistics and finance, to make use of quantum-enhanced solutions without building up expensive interdisciplinary expertise.

Challenge: Making quantum-enhanced solutions accessible

Quantum computing is a field for experts: It requires specific algorithm design and inter- disciplinary knowledge in problem formulation, algorithm selection, parameter tuning, and postprocessing. Industrial end users need efficient, performant ways to set up quantum-enhanced solutions. Guidance by a modular, state-of-the-art recommendation system starting at the industry application is needed to unlock the potential of the technology.

Solution approach: The QuaST Decision Tree

The many options in setting up a quantum-enhanced solution are organized in a systematic decision tree. Its nodes form a modular, flexible computation framework capable of connecting to backends and specialized tools for pre- and postprocessing. In a forward pass starting at the application, a quantum algorithm is set up and can be executed on simulators or hardware. A backward pass allows postprocessing and decoding to ultimately yield a quantum-enhanced solution. The framework can be easily expanded and configured to specific tasks: forming the application layer of a QC software stack, integrating with commercial solvers, rapid prototyping in a R&D team, or simply exploring QC technologies and building up competences.

Key Benefits:

• Easier use of quantum-enhanced solutions: End users can concentrate on their applications instead of building up interdisciplinary expertise in setting up quantum-enhanced solutions.
• Reduced try-and-error overhead: The costly testing, optimizing and re-testing of quantum-enhanced algorithms is reduced.
• Flexible adaptation and configuration: The QuaST Decision Tree can be integrated into existing workflows and use external tools by incorporating them as nodes, including modified input and output for further processing in frontend or backend.

Insight Background

Our mission is technology transfer. In quantum computing, this means combining classical and quantum parts to a full algorithm with measurable benefits for logistics, finance and supply chain tasks. With the QuaST Decision Tree, we build a bridge between application and technology – a crucial component that is needed to bring quantum computing into its productive phase!

In-depth information

• The scientific concept behind the decision tree

• An in-depth description of the QuaST decision tree

• How to decide whether an algorithm can provide a benefit

• Making quantum computing useful for optimization: Results from the QuaST project (including a Best Practice Guide)

Sponsored by

Federal Ministry for Economic Affairs and Climate Action on the basis of a decision by the German Bundestag through the project QuaST (reference number: 01MQ22004D)