Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems

Understand

Fourier Neural Operators (FNOs) excel on tasks using functional data, such as those originating from partial differential equations.

• Such characteristics render them an effective approach for simulating the time evolution of quantum wavefunctions, which is a computationally challenging, yet coveted task for studying quantum systems.
• In this manuscript, we use FNOs to model the evolution of quantum spin systems, so chosen due to their representative quantum dynamics.
• We explore two distinct FNO architectures, examining their performance for learning and predicting time evolution on both random and low-energy input states.