Predict and Conquer: Navigating Algorithm Trade-offs with Quantum Design Automation

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.