Abstract: Traditional approaches for recommending optimum combination of quantum circuits are experimentation based approaches, and require manual efforts or are cumbersome, effort intensive and iterative processes. Method and system disclosed herein generally relates to quantum experimentation, and, more particularly, for recommending optimum combination of quantum circuits. In this approach, a high-level combination of experiments are initially generated, which are further prioritized using a graph based approach, which then forms a training data. The training data is then used for generating a GNN data model, which is further used for recommending optimum combination of quantum circuits.