Computational complexity of isometric tensor network states
<|reference_start|>Computational complexity of isometric tensor network states: We determine the computational power of isometric tensor network states (isoTNS), a variational ansatz originally developed to numerically find and compute properties of gapped ground states and topological states in two dimensions. By mapping 2D isoTNS to 1+1D unitary quantum circuits, we find that computing local expectation values in isoTNS is $\textsf{BQP}$-complete. We then introduce injective isoTNS, which are those isoTNS that are the unique ground states of frustration-free Hamiltonians, and which are characterized by an injectivity parameter $\delta\in(0,1/D]$, where $D$ is the bond dimension of the isoTNS. We show that injectivity necessarily adds depolarizing noise to the circuit at a rate $\eta=\delta^2D^2$. We show that weakly injective isoTNS (small $\delta$) are still $\textsf{BQP}$-complete, but that there exists an efficient classical algorithm to compute local expectation values in strongly injective isoTNS ($\eta\geq0.41$). Sampling from isoTNS corresponds to monitored quantum dynamics and we exhibit a family of isoTNS that undergo a phase transition from a hard regime to an easy phase where the monitored circuit can be sampled efficiently. Our results can be used to design provable algorithms to contract isoTNS. Our mapping between ground states of certain frustration-free Hamiltonians to open circuit dynamics in one dimension fewer may be of independent interest.<|reference_end|>
@article{malz2024computational,
    title={Computational complexity of isometric tensor network states},
    author={Daniel Malz and Rahul Trivedi},
    journal={arXiv preprint arXiv:2402.07975},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.07975},
    primaryClass={quant-ph cs.CC}
}