Machine learning can be used for generative methods that approximate PDFs corresponding to quantum field theories. To remove any bias an accept/reject step is required which for theories with fermions involve the calculation of the fermionic determinant. We investigate the use of pseudo-fermion methods for the accept/reject step to bypass the need to compute costly determinants. As an example...
In this talk I will introduce persistent homology, a tool from the emerging field of topological data analysis, and demonstrate how it can be used to produce new observables of lattice spin models. In particular, I will talk about recent work on developing a persistent homology-based methodology to extract the critical temperature and critical exponent of the correlation length of phase...