Abstract: Optimal Transport (OT) has fueled machine learning (ML) applications across various domains. In cases where paired data measurements ($\mu$, $\nu$) are coupled to a context variable $p_i$ , one may aspire to learn a global transportation map, parameterized through the context to facilitate prediction of target states even from unseen context. Existing approaches for this task leverage Brenier’s theorem and utilize Neural OT. Here, we follow a radically different approach inspired by quantum computing principles to develop a Quantum formulation for learning transportation plans parameterized by a context variable. This is achieved through exploiting a natural link between doubly stochastic matrices and unitary operators. The latter can be directly related to recent results in quantum learning theory suggesting intrinsic advantages in modelling constrained problems with quantum methods. We verify our methodology on synthetic data, emulating the task of predicting single-cell perturbation responses parameterized through drug dosage as context. Our experimental comparisons to a baseline reveal that our method can capture dose-induced variations in cell distributions, even to some extent when extrapolating to dosages outside the interval seen during training. In summary, this work assesses the feasibility of learning to predict contextualized transportation plans through a novel quantum computing approach.