Quantum computing is a new technology that depends on quantum mechanics concepts to solve traditional problems in computer science in different domains such as optimization, simulation and machine learning computations. The benefit of quantum computers over classical ones is that quantum algorithms provide an exponential speed-up over classical algorithms. This extraordinary promising performance motivates vendors and researchers to develop solutions using quantum computing and integrate them to solve different use cases. However, quantum computers depend on a different implementation than classical computers by using quantum circuits and data preparation and do not handle classical bits by central computations. Thus, we need to revise the translation process of high-level requirements and how to implement it as quantum solutions. For this reason, the integration and deployment of quantum computers might be expensive and not all problems can not benefit from it. Thus, In this early stage, stakeholders, e.g. researchers and enterprises, need support in this process to decide whether to use quantum or classical computers. To address this problem, we present a concept of using a decision tree to help users in this process. The decision tree is based on results that have been reviewed from literature which started to use quantum computers and compare their performance with classical methods. It consists of a set of decision which is connected consequently, starting from describing the use case requirements and transforming them into a mathematical formulation which can be solved on quantum computers. In addition, this project investigated the content comparison of the different use cases using NLP methods to get similar cases together and provide a recommendation based on similar content. The decision tree must be accessible in an easy way to all users from different backgrounds, including researchers, technical, vendors, and decision-makers in the business domain. To validate the practical feasibility of our concept, we introduce QuCSplit as a support framework to help users make their decisions. The decision tree approach can provide a reliable model of linear decisions that are sequentially ordered. However, it is still difficult to include decisions that require complex calculations and relationships. Furthermore, the texts comparison method still seeks a reliable evaluation study to analyze its accuracy.