To solve specific problems using quantum computing requires (1) the selection of a suitable quantum algorithm, (2) the generation of a corresponding encoding, (3) the compilation/execution of the resulting quantum circuit, and (4) decoding the results. Furthermore, in cases where problem instances are too large for current quantum devices, alternative approaches are required to evaluate their feasibility and estimate the required resources. To date, these tasks still substantially rely on manual labour and expertise in quantum computing is required for all of these steps—creating a high entry barrier, especially for users with little to no expertise in the domain of quantum computing. However, as research in the field of quantum computing is gradually improving with respect to both physical devices and logical algorithms, and their accessibility becomes more and more widespread through service providers such as IBM or Amazon Web Services, interest in the application of quantum algorithms to solve a large variety of different problems is spreading rapidly to an equally diverse range of users. While these users may have deep knowledge in their respective domains, they are not necessarily guaranteed to be as experienced in the area of quantum computing, and thus, the development of tools that aid end users by shielding them from concepts requiring deeper knowledge of quantum computing has grown in relevance. In this talk proposal, several methods and tools developed as part of the Munich Quantum Toolkit (MQT, https://mqt. readthedocs.io/en/latest/) for supporting end-users in conducting those tedious and error-prone steps are discussed. They aim to shield the end-user as much as possible from quantum computing specifics through automation and guidance. All software is available as open-source on GitHub at https://github.com/cda-tum/.