Quantum and classical networking between modular quantum processors enable the construction of large-scale distributed quantum computers with capabilities beyond the reach of a monolithic quantum computer. Quantum networking allows distributing the entangling operations with gate teleportation via shared entanglement whereas classical networking allows distribution via circuit knitting with an overhead in the number of circuit repetitions. Both techniques have their own bottlenecks - quantum networking technology today suffers from poor Bell state fidelities and slow rate of entanglement generation, while classical networking suffers from exponential sampling overhead, in the worst case. We can partially overcome these bottlenecks by utilizing both classical and quantum interconnects simultaneously via entanglement-assisted circuit partitioning. In this talk, I will introduce two distinct classical preprocessing algorithms that can significantly lower both the classical and quantum distribution overhead for quantum simulation, one of the most fundamental applications of quantum computing. The first algorithm performs efficient distribution of quantum dynamics using compressed tensor network representation. Similarly, the second algorithm disentangles from the tensor network a circuit that can exponentially lower the classical distribution overhead. We demonstrate both these algorithms via numerical simulation of the disordered quantum Ising dynamics achieving orders of magnitude improvement in distribution complexity over the theoretical lower bound based on LOCC-only approaches. Our work establishes classical preprocessing as a path towards large-scale distributed quantum simulations with hybrid classical and quantum interconnects.