  We explain why and numerically confirm that there are no barren plateaus in
the energy optimization of isometric tensor network states (TNS) for extensive
Hamiltonians with finite-range interactions which are, for example, typical in
condensed matter physics. Specifically, we consider matrix product states (MPS)
with open boundary conditions, tree tensor network states (TTNS), and the
multiscale entanglement renormalization ansatz (MERA). MERA are isometric by
construction and, for the MPS and TTNS, the tensor network gauge freedom allows
us to choose all tensors as partial isometries. The variance of the energy
gradient, evaluated by taking the Haar average over the TNS tensors, has a
leading system-size independent term and decreases according to a power law in
the bond dimension. For a hierarchical TNS (TTNS and MERA) with branching ratio
$b$, the variance of the gradient with respect to a tensor in layer $\tau$
scales as $(b\eta)^\tau$, where $\eta$ is the second largest eigenvalue of a
Haar-average doubled layer-transition channel and decreases algebraically with
increasing bond dimension. The absence of barren plateaus substantiates that
isometric TNS are a promising route for an efficient quantum-computation-based
investigation of strongly-correlated quantum matter. The observed scaling
properties of the gradient amplitudes bear implications for efficient TNS
initialization procedures.
