  We present an open-source tensor network Python library for quantum many-body
simulations. At its core is an abelian-symmetric tensor, implemented as a
sparse block structure managed by logical layer on top of dense
multi-dimensional array backend. This serves as the basis for higher-level
tensor networks algorithms, operating on matrix product states and projected
entangled pair states, implemented here. Using appropriate backend, such as
PyTorch, gives direct access to automatic differentiation (AD) for
cost-function gradient calculations and execution on GPUs or other supported
accelerators. We show the library performance in simulations with infinite
projected entangled-pair states, such as finding the ground states with AD, or
simulating thermal states of the Hubbard model via imaginary time evolution. We
quantify sources of performance gains in those challenging examples allowed by
utilizing symmetries.
