Machine learning Majorana topology using unsupervised and supervised learning
The study uses unsupervised learning (autoencoder and k-means) to find hidden patterns in unlabeled data from disordered nanowires, followed by supervised learning to label them topologically. This combined approach successfully identifies three distinct phases: topological, trivial, and an intermediate crossover regime. This method helps classify experimental Majorana data.