Fig. 3: A general algorithm for learning properties of quantum states in the non-i.i.d. setting.
From: Learning properties of quantum states without the IID assumption
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 \({\rho }_{c,p}^{{A}_{N}}\).
