Until scalable fault-tolerant quantum computing becomes a reality, quantum algorithms will continue to depend heavily on noise mitigation techniques. In this talk, I will present our Tensor-Network-based Error Mitigation (TEM) algorithm, a post-processing approach designed to correct noise-induced errors in the estimation of physical observables. The method involves constructing a tensor network representation of the inverse of the global noise channel affecting the quantum processor's state, and subsequently applying this inverse map to informationally complete measurement data obtained from the noisy state. I will walk through the details of the algorithm and share recent results, including applications to 91-qubit circuits involving over 4,000 CNOT gates.