What non-symmetric measure from information theory is used to quantify the difference between a target probability distribution P and an approximation Q?

Kullback-Leibler divergence

Kullback-Leibler (KL) Divergence measures the 'information gain' or 'relative entropy' between two distributions. In ML, it is frequently used in the objective function of VAEs and for comparing soft targets in knowledge distillation.