Using transfer learning to generate samples for large systems of the spin-fermion Hamiltonian

We are expanding on our previous work where we used neural networks to generate samples for the spin-fermion Hamiltonian. The main bottleneck of our previous work is generating the training data set requires time and memory resources that scale unfavorably as the sytem's size. We present a transfer learning approach in which we train neural networks on smaller systems and by appropriately modifying them we build models that can generate samples for far larger systems.