This paper proposes a Quantum Computational Intelligence (QCI) robot with a Generative AI (GAI) knowledge graph (KG) for Taiwanese and Japanese co-learning model applications. During the 2024 IEEE CIS Summer School on QCI at Tokyo Metropolitan University (TMU) in Japan, we organized lectures and a hands-on workshop on QCI for young students to learn and experience QCI using the QCI&AI-FML learning tool and robot. Learners first observe, study, research, utilize, understand, and explain what they have learned in the heart-sutra-based human and machine co-learning model. We transcribed the collected multimodal data using the OpenAI Whisper models into English or Japanese texts. The GAI Knowledge Graph (GAIKG) agent generates the knowledge graph with concepts, relations, and communities for the transcribed texts by providing prompts with Large Language Models (LLMs), such as Trustworthy AI Dialogue Engine (TAIDE). The Sentence BERT (SBERT) similarity agent computes the similarity between the collected texts and the golden standard provided by the English/Japanese domain experts. Next, CI domain experts constructed the CI model based on the generated data from these two agents to infer the performance of the generated knowledge graph and deployed the model to the QCI robot. Finally, the QCI robot evaluates how well the generative knowledge graph leverages human and machine learning according to the constructed CI model. In addition, the QCI robot evaluates the performance of the generative knowledge graph in co-learning English/Japanese based on the constructed CI model and quantum fuzzy inference engine. In the future, we will extend the QCI robot to more countries for young students to co-learn CI, QCI, and Taiwanese/Japanese languages with smart machines.