This work presents a method for fast protein backbone generation using SE(3) flow matching.Specifically, we adapt FrameDiff, a state-of-the-art non-pretrained diffusion model, to perform flow matching with minimal changes.We first develop the theoretical results for SE(3) flow matching then demonstrate modifications during training to effectively learn the conditional vector field.Compared to FrameDiff, we require five times less timesteps to sample while achieving the same designability metrics on unconditional monomer backbone generation.Our work paves way towards faster generative models in de novo protein design.