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arXiv:2605.18539

The QuaST Decision Tree: Achieving Automation With Data-Based Recommendations

Benedikt Poggel, Lena Tokuhiro, Georg Kruse, Jeanette Miriam Lorenz

quant-phSubmitted May 18, 2026• Announced May 19, 2026• 0 citations
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Abstract

Quantum computers are increasingly powerful. Software tools for the development of quantum-enhanced algorithms are maturing. However, the software stack still lacks the connection to applications that would enable hybrid algorithms combining classical and quantum computing steps. End users need to be assisted in choosing the best combination of preprocessing, postprocessing, classical and quantum algorithms options. The application-facing software stack is therefore required to cover problem modeling, encoding, algorithm selection and hyperparameter tuning. A variety of tools exist for specific recommendations. The QuaST Decision Tree reflects the complexity in combining individual decisions in its modular network structure, consisting of flexible computation nodes with modular recommendations. It can easily be configured to serve in an industrial solver, an HPC software stack, or for rapid prototyping in development. The key ingredient, automation, is delivered by modules. We present one such module judging the feasibility of variational algorithms based on a robust scalability analysis and classification of problem instances. The automation improves the performance of end-to-end solutions, highlights the benefit to be gained from the hybrid quantum solution, reduces expensive trial-and-error testing, and leads to an improved utilization of quantum devices for a practical benefit.

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Submitted to the IEEE for possible publication

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Combining quantum and classical computing is expected to beat purely classical approaches for specific use cases. Howver, non-functional properties like runtime or solution quality of many quantum-classical algorithms can only be measured empirically, which makes finding the best approach for a given problem difficult. Predicting behaviour of quantum-classical algorithms opens possibilities for software abstraction layers, which in turn can automate decision-making for algorithm selection and parametrisation. These techniques are common in classical computing but still mostly absent in quantum toolchains. This work, originally published at the IEEE Conference on Quantum Computing and Engineering (QCE) 2025 [TM25], presents a methodology for automatic quantum algorithm selection based on non-functional requirements, simplifying decision-making for end users. Based on meta-information annotations at the source code level, our framework traces key characteristics of quantum-classical algorithms to predict the best approach and its parameters for a given problem and non-functional requirements. To validate our ideas, we perform a comprehensive case study for combinatorial optimisation and develop statistical predictors for algorithmic behaviour to automatically choose the best algorithm for a given scenario.
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