Order Parameter Discovery for Quantum Many-Body Systems
Quantum phase transitions are difficult to identify without prior knowledge of order parameters. This work introduces a method that constructs phase diagrams using the vector field of the reduced fidelity susceptibility (RFS) and demonstrates how this vector field can be used to discover observables corresponding to order parameters. The approach is applied to the ANNNI model, a cluster state model, and a chain of Rydberg atoms, with validation via eigendecomposition and finite-size scaling confirming expected universality classes.
Quantum phase transitions are difficult to identify without prior knowledge of order parameters. This work introduces a method that constructs phase diagrams using the vector field of the reduced fidelity susceptibility (RFS) and demonstrates how this vector field can be used to discover observables corresponding to order parameters. The approach is applied to the ANNNI model, a cluster state model, and a chain of Rydberg atoms, with validation via eigendecomposition and finite-size scaling confirming expected universality classes.
The RFS vector field framework offers a data‑driven, symmetry‑agnostic tool that could support Swiss AI research in quantum technologies by enabling automated discovery of phase transitions from small subsystem data.
Original source record. Open the original record to verify the underlying announcement.