Adaptivity is all you need: Optimal stabilizer learning using just single-copy measurements
This work shows that a polynomial-time adaptive algorithm can learn an arbitrary n-qubit stabilizer state using only Theta(n) single-copy Clifford measurements, matching the optimal sample complexity of Bell sampling without needing multiple copies. The method involves adaptively applying Clifford operations based on measurement outcomes to transform non-diagonal stabilizers into Z-type ones.