The QuaST Decision Tree: Achieving Automation With Data-Based Recommendations
Benedikt Poggel, Lena Tokuhiro, Georg Kruse, Jeanette Miriam Lorenz

TL;DR
The QuaST Decision Tree is a modular, data-driven framework designed to automate and optimize the selection and combination of classical and quantum algorithms for hybrid quantum computing applications.
Contribution
It introduces a flexible, modular decision tree structure that automates algorithm selection and problem feasibility assessment in quantum computing workflows.
Findings
Automates the decision-making process for hybrid quantum algorithms.
Enhances end-to-end solution performance and quantum device utilization.
Reduces trial-and-error testing through robust scalability analysis.
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…
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