Tomography of time-dependent quantum spin networks with machine learning

Understand

Interacting spin networks are fundamental to quantum computing.

• Data-based tomography of time-independent spin networks has been achieved, but an open challenge is to ascertain the structures of time-dependent spin networks using time series measurements taken locally from a small subset of the spins.
• Physically, the dynamical evolution of a spin network under time-dependent driving or perturbation is described by the Heisenberg equation of motion.
• Motivated by this basic fact, we articulate a physics-enhanced machine learning framework whose core is Heisenberg neural networks.