Researchers have developed a method to simulate complex quantum systems using tensor networks. This approach allows for the efficient simulation of continuous-time generative models, which are widely used in applications such as computer vision and protein folding. The method uses wavefunction flows to recast learned transport as unitary evolution, reducing computational costs by up to 10^7 times compared to traditional methods. --- Why it matters: This research is important for engineers working on quantum algorithms because it provides a scalable solution for simulating complex systems, which could lead to breakthroughs in fields like materials science and chemistry. Source: https://arxiv.org/abs/2608.21700