Matrix-product entanglement characterizing the optimality of state-preparation quantum circuits

Multipartite entanglement offers a powerful framework for understanding the complex collective phenomena in quantum many-body systems that are often beyond the description of conventional bipartite entanglement measures. Here, we propose a class of multipartite entanglement measures that incorporate the matrix product state (MPS) representation, enabling the characterization of the optimality of quantum circuits for state preparation. These measures are defined as the minimal distances from a target state to the manifolds of MPSs with specified virtual bond dimensions $χ$, and thus are dubbed as $χ$-specified matrix product entanglement ($χ$-MPE). We demonstrate superlinear, linear, and sublinear scaling behaviors of $χ$-MPE with respect to the negative logarithmic fidelity $F$ in state preparation, which correspond to excessive, optimal, and insufficient circuit depth $D$ for preparing $χ$-virtual-dimensional MPSs, respectively. Specifically, a linearly-growing $χ$-MPE with $F$ suggests $\mathcal{H}_χ \simeq \mathcal{H}_{D}$, where $\mathcal{H}_χ$ denotes the manifold of the $χ$-virtual-dimensional MPSs and $\mathcal{H}_{D}$ denotes that of the states accessible by the $D$ layer circuits. We provide an exact proof that $\mathcal{H}_{χ=2} \equiv \mathcal{H}_{D=1}$. Our results establish tensor networks as a powerful and general tool for developing parametrized measures of multipartite entanglement. The matrix product form adopted in $χ$-MPE can be readily extended to other tensor network ansätze, whose scaling behaviors are expected to assess the optimality of quantum circuit in preparing the corresponding tensor network states.