Tensor networks (TNs) offer powerful algorithms for simulating quantum systems, but struggle to represent states with high entanglement. In contrast, quantum computers naturally accommodate entanglement, though developing efficient quantum algorithms remains an active area of research. Integrating TNs with quantum computing has emerged as a promising strategy to overcome limitations inherent to both classical and quantum approaches. In this talk, we will cover two examples of hybrid TN/quantum algorithms. First, we demonstrate how TNs can enhance the simulation of quantum dynamics on noisy quantum devices. In particular, we use a TN algorithm to compress quantum circuits and show that this approach significantly reduces noise requirements to reach a practical advantage on noisy hardware. Second, we explore how TNs can assist in preparing approximate ground states on quantum computers by optimizing parameterized quantum circuits. We show that carefully selecting TN algorithms enables scaling to large qubit systems and that pre-optimizing circuits offers a promising strategy to avoid barren plateaus by providing warm-start initialization. Finally, we analyze the classical simulation costs of this approach and identify scenarios where quantum computers exhibit favorable scaling.