Fig. 6: The schematics of three different transfer learning scenarios used in this study are presented, along with the corresponding histogram showing the number of epochs required for convergence with the transfer learning strategy.

The neural-network icons represent the NNQS solver and the green arrows indicate the direction to transfer neural-network parameters. a In the DMET iteration process, each μ iteration step involves loading the model from the preceding step, and similarly, each u iteration loads the model from the previous step. b Transfer learning is applied to embedding Hamiltonians generated by the hydrogen chain with varying H-H distance values. c Transfer learning is utilized between similar fragments in bulk diamonds.