Kavli Affiliate: Eliska Greplova
| First 5 Authors: Vinicius Hernandes, Thomas Spriggs, Saqar Khaleefah, Eliska Greplova,
| Summary:
Neural quantum states (NQS) have emerged as a powerful tool for approximating
quantum wavefunctions using deep learning. While these models achieve
remarkable accuracy, understanding how they encode physical information remains
an open challenge. In this work, we introduce adiabatic fine-tuning, a scheme
that trains NQS across a phase diagram, leading to strongly correlated weight
representations across different models. This correlation in weight space
enables the detection of phase transitions in quantum systems by analyzing the
trained network weights alone. We validate our approach on the transverse field
Ising model and the J1-J2 Heisenberg model, demonstrating that phase
transitions manifest as distinct structures in weight space. Our results
establish a connection between physical phase transitions and the geometry of
neural network parameters, opening new directions for the interpretability of
machine learning models in physics.
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