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

Quantum computers are becoming increasingly powerful, with software tools for developing quantum-enhanced algorithms also maturing rapidly. However, a significant gap remains in the software stack, as it lacks connection to applications that enable hybrid algorithms combining classical and quantum computing steps. To address this, researchers have introduced the QuaST decision tree, a framework designed to provide data-based recommendations for choosing the optimal combination of preprocessing, postprocessing, classical, and quantum computing steps. This decision tree aims to automate the process of selecting the best approach for specific applications, thereby facilitating the development of hybrid algorithms. The QuaST decision tree has the potential to significantly enhance the usability of quantum computers by providing end users with guidance on optimizing their workflows¹. This development matters to practitioners because it could finally enable the widespread adoption of quantum computing by making it more accessible and user-friendly.