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Majorana zero modes (MZMs), emerging as exotic quasiparticles that carrynon-Abelian statistics, hold great promise for achieving fault-toleranttopological quantum computation. A key signature of the presence of MZMs is thezero-bias peaks (ZBPs) from tunneling differential conductance. However, theidentification of MZMs from ZBPs has faced tremendous challenges, due to thepresence of topological trivial states that generate spurious ZBP signals. Inthis work, we introduce a machine-learning framework that can discern MZM fromother signals using ZBP data. Quantum transport simulation from tight-bindingmodels is used to generate the training data, while persistent cohomologyanalysis confirms the feasibility of classification via machine learning. Inparticular, even with added data noise, XGBoost classifier reaches $85\%$accuracy for 1D tunneling conductance data and $94\%$ for 2D data incorporatingZeeman splitting. Tests on prior ZBP experiments show that some data are morelikely to originate from MZM than others. Our model offers a quantitativeapproach to assess MZMs using ZBP data. Furthermore, our results shed light onthe use of machine learning on exotic quantum systems withexperimental-computational integration.

Machine learning detecting Majorana Zero Mode from Zero Bias Peak measurements