Extracting complexity of quantum dynamics using machine learning

Zala Lenarcic (Jozef Stefan Institute, Ljubljana)

Dec 01. 2021, 14:35 — 15:10

The vast complexity is a daunting property of generic quantum states that poses a significant challenge for theoretical treatments, especially in non-equilibrium setups. Therefore, it is vital to recognize states which are locally less complex and thus describable with (classical) effective theories. 

I will discuss how unsupervised learning can detect the local complexity of states. This approach can be used as a probe of scrambling, hydrodynamics and thermalization in chaotic quantum systems or to assign the local complexity in open setups without knowing the corresponding Hamiltonian or Liouvillian. The analysis actually allows for the reconstruction of Hamiltonian operators or even noise-type that might be contaminating the measurements. Since our approach requires only easily accessible local observations, it is an ideal diagnostics tool for data obtained from (noisy) quantum simulators.

Further Information
Erwin Schrödinger Institute - virtual
Associated Event:
Topology, Disorder, and Hydrodynamics in Non-equilibrium Quantum Matter (Online Workshop)
Jörg Schmiedmayer (TU Vienna)
Maksym Serbyn (ISTA, Klosterneuburg)
Romain Vasseur (UMass Amherst)