Two-dimensional non-Hermitian skin effect in an ultracold Fermi gas.

The concept of non-Hermiticity has expanded the understanding of band topology, leading to the emergence of counter-intuitive phenomena. An example is the non-Hermitian skin effect (NHSE), which involves the concentration of eigenstates at the boundary. However, despite the potential insights that can be gained from high-dimensional non-Hermitian quantum systems in areas such as curved space, high-order topological phases and black holes, the realization of this effect in high dimensions remains unexplored.

Observing a topological phase transition with deep neural networks from experimental images of ultracold atoms.

Although classifying topological quantum phases have attracted great interests, the absence of local order parameter generically makes it challenging to detect a topological phase transition from experimental data. Recent advances in machine learning algorithms enable physicists to analyze experimental data with unprecedented high sensitivities, and identify quantum phases even in the presence of unavoidable noises. Here, we report a successful identification of topological phase transitions using a deep convolutional neural network trained with low signal-to-noise-ratio (SNR) experimental data obtained in a symmetry-protected topological system of spin-orbit-coupled fermions.