This chapter discusses the properties of tree tensor network states and the methods for evaluating the ground state and thermodynamic properties of quantum lattice models on a Bethe lattice or, more generally, a Husimi lattice. It starts with a brief discussion of the canonical form of a tree tensor network state. Then, a canonicalization scheme is proposed. To calculate the ground state through the imaginary time evolution, the full and simple update methods are introduced to renormalize the local tensors. Finally, as the correlation length of a quantum system is finite even at a critical point, an accurate and efficient method is described to compute the thermodynamic quantities of quantum lattice models on the Bethe lattice.

This chapter constructs the MPS representation of a quantum state in the continuous limit. It starts with an MPS representation for the corresponding state in a discretized lattice system. Then the limit of the discretized lattice constant going to zero is taken to obtain its continuous presentation. The formulas for determining the expectation values are also derived. Finally, we discuss the scheme of canonicalization and the method for optimizing the local tensors of the continuous MPS.

This chapter discusses the truncation criteria in the RG treatment of a non-Hermitian matrix, starting with a modified definition of the reduced density matrix using the leading left and right eigenvectors. As the reduced density matrix so defined is not Hermitian, there is no theorem to protect or guarantee that its eigenvalues are semi-positive definite. This non-Hermitian problem causes trouble in the determination of an optimized truncation scheme. Three truncation schemes for determining the RG transformation matrices are introduced, relying on the canonical diagonalization of the reduced density matrix, biorthonormalization, and lower-rank approximation of the environment density matrix, respectively. The canonical diagonalization scheme is optimal if the reduced density matrix is semi-positive definite. The scheme of biorthonormalization may not be optimal, but it is mathematically more stable.