In recent years, an interesting interplay between the rise of Quantum Computing and AI has emerged. AI is being used to optimize quantum computation and quantum computing is being used to speed up AI. One aspect of AI that has gained attention in the last decade, namely artificial curiosity or intrinsic motivation, has yet to be integrated with quantum computing. Moreover, recently, the curiosity loop was introduced, which includes a machine learning algorithm that provides intrinsic reward for a reinforcement learning agent, thereby optimizing the learning process. The curiosity loop has been used in the Curious Data Scientist Framework, including curious feature selection, curious instance selection and curiosity-based clustering. Here we propose an extension of this framework to include both quantum machine learning and quantum reinforcement learning algorithms within the curiosity loop architecture. We present a novel quantum curious feature selection algorithm within this framework as a first step in the introduction of Quantum Curiosity. Discussion and future extensions of this quantum framework are presented.