This project aims to implement geometric deep learning on variational quantum algorithms to handle symmetric input data efficiently. To this end, we have constructed a specialized quantum machine learning setup which is able to handle molecular data efficiently. We have run some small-scale pilot tests with up to 16qbits on our laptops to simulate this system with promising results. But in order to validate our setup thoroughly, we need to run further simulations on scales beyond the capabilities of our laptops.