Quantum Phase Transition Discovery with Diffusion Maps. Supporting code for "Unsupervised Machine Learning of Quantum Phase Transitions Using Diffusion Maps": https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.125.225701. Unfortunately, most of the data files for the large system sizes and the chiral clock model used to generate the figures were too large to upload, but these are the codes that generated them.
The Exact_Diagonalization folder contains ED simulations of quantum-many-body models and will use the generated samples to perform diffusion maps (among other unsupervised dimension reduction and clustering techniques such as PCA and autoencoders with k-means). The MPS_Postprocessing uses a set of optimized Matrix Product States (generated by using the OSMPS library), draws samples, and performs diffusion maps on said samples. The Utils folder contains the supporting functions used for ED, MPS processing, dimension reduction, clustering, and plotting.