One of the non-recursive approaches was learning outcome of dynamics (transfer time and efficiency) rather than dynamics. Recently, we have proposed an alternative non-recursive, artificial intelligence-based quantum dynamics (AI-QD) approach, learning dynamics trajectories as a function of time and simulation parameters: the reorganization energy λ, characteristic frequency γ, and temperature T . This makes all time-steps independent from each other which allows us to predict system's state at any arbitrary time, without the need of propagating the trajectory. Despite a significant speed-up of dynamics propagation with our AI-QD approach, gener-ating the entire trajectory still needs the evaluation of ML function at each required time-step of the trajectory, making it relatively slow. In addition, the time and memory requirements for training are rather high because of the way the data for supervised learning is prepared: AI-QD is trained on as many points as there are time-steps in all training trajectories, i.e., training set contains millions of points. Here we propose a one-shot trajectory learning (OSTL) quantum dynamics approach, which is a much more efficient strategy for both learning and trajectory propagation than AI-QD. Similar to AI-QD, OSTL takes the simulation parameters as input but then in contrast to AI-QD, OSTL directly evaluates the entire trajectory as a multi-output prediction by a one-dimensional (1D) convolutional neural network (CNN) (Fig. ). The time range of the entire trajectory is set by the maximum run time of the training trajectories. Our new strategy only requires up to 70 milliseconds for 10 ps long trajectory for the reduced density matrix ρ(t) in the seven sites FMO complex 2,3 which was also used in previous ML studies. OSTL also has significantly reduced computational requirements for training as a multi-output 1D CNN is trained on the number of points equal to the number of training trajectories.