Fig. 4: Illustration of Algorithm 1.
From: Learning properties of quantum states without the IID assumption
Algorithm 1 measures a large number of the state’s subsystems using \({{{{\mathcal{M}}}}}_{{{{\bf{r}}}}}^{{{{\mathcal{A}}}}}\) that represents measurement devices uniformly chosen from the i.i.d. algorithm’s set of measurements (red and green parts). Then, Algorithm 1 applies the data processing of Algorithm \({{{\mathcal{A}}}}\) to the outcomes of a part of these subsystems (green part), leading to a prediction p. Algorithm 1 returns the remaining outcomes as calibration w. Success occurs if p is (approximately) compatible with the remaining post-measurement test copy \({\rho }_{l,{{{\bf{r}}}},{{{\bf{w}}}},p}^{{A}_{N}}\).
