Tensor Network Monte Carlo for Quantum Lattice Models

Tensor networks can accurately and efficiently encode the ground states and thermal density matrices of local quantum lattice Hamiltonians. However, extracting expectation values from these representations is known to require the use of approximation schemes; in this work we combine recently developed Monte Carlo techniques for tensor networks with more standard Renormalization Group approaches. We present preliminary results and discuss the current status of our efforts to generalize these methods.