  Recent progress in the design and optimization of Neural-Network Quantum
States (NQS) have made them an effective method to investigate ground-state
properties of quantum many-body systems. In contrast to the standard approach
of training a separate NQS from scratch at every point of the phase diagram, we
demonstrate that the optimization of a NQS at a highly expressive point of the
phase diagram (i.e., close to a phase transition) yields interpretable features
that can be reused to accurately describe a wide region across the transition.
We demonstrate the feasibility of our approach on different systems in one and
two dimensions by initially pretraining a NQS at a given point of the phase
diagram, followed by fine-tuning only the output layer for all other points.
Notably, the computational cost of the fine-tuning step is very low compared to
the pretraining stage. We argue that the reduced cost of this paradigm has
significant potential to advance the exploration of condensed matter systems
using NQS, mirroring the success of fine-tuning in machine learning and natural
language processing.
