  Solving real-world optimization problems with quantum computing requires
choosing between a large number of options concerning formulation, encoding,
algorithm and hardware. Finding good solution paths is challenging for end
users and researchers alike. We propose a framework designed to identify and
recommend the best-suited solution paths. This introduces a novel abstraction
layer that is required to make quantum-computing-assisted solution techniques
accessible to end users without requiring a deeper knowledge of quantum
technologies. State-of-the-art hybrid algorithms, encoding and decomposition
techniques can be integrated in a modular manner and evaluated using
problem-specific performance metrics. Equally, tools for the graphical analysis
of variational quantum algorithms are developed. Classical, fault tolerant
quantum and quantum-inspired methods can be included as well to ensure a fair
comparison resulting in useful solution paths. We demonstrate and validate our
approach on a selected set of options and illustrate its application on the
capacitated vehicle routing problem (CVRP). We also identify crucial
requirements and the major design challenges for the proposed automation layer
within a quantum-assisted solution workflow for optimization problems.
