Multi-hop question generation (QG) is the process of generating answer related questions, which requires aggregating multiple pieces of information and reasoning from different parts of the texts. This is opposed to single-hop QG which generates questions from sentences containing an answer in a given paragraph. Single-hop QG requires no reasoning or complexity, while multi-hop QG often requires logical reasoning to derive an answer related question, making it a dual task. Not enough research has been made on the multi-hop QG due to its complexity. Also, a question should be created using the question type and words related to the correct answer as a prompt so that multi-hop questions can get more information. In this view, we propose a new type-dependent prompt cycleQAG (cyclic question-answer-generation), with a cycle consistency loss in which QG and Question Answering (QA) are learnt in a cyclic manner. The novelty is that the cycle consistency loss uses the negative cross entropy to generate syntactically diverse questions that enable selecting different word representations. Empirical evaluation on the multi-hop dataset with automatic and human evaluation metrics outperforms the baseline model by about 10.38% based on ROUGE score.