Fig. 3. A general algorithm for learning properties of quantum states in the non-i.i.d. setting.

A learning algorithm $\mathcal{B}$ takes as input the N−1 copies of the train set and returns a prediction p and a calibration c. Success occurs if p is (approximately) compatible with the remaining post-measurement test copy ${\rho }_{c,p}^{A_N}$.