Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadows

arXiv:2402.17911v1 Announce Type: cross
Abstract: Extracting information efficiently from quantum systems is a major component of quantum information processing tasks. Randomized measurements, or classical shadows, enable predicting many properties of arbitrary quantum states using few measurements. While random single qubit measurements are experimentally friendly and suitable for learning low-weight Pauli observables, they perform poorly for nonlocal observables. Prepending a shallow random quantum circuit before measurements maintains this experimental friendliness, but also has favorable sample complexities for observables beyond low-weight Paulis, …

abstract arxiv cond-mat.stat-mech cs.it cs.lg information low major math.it processing property quant-ph quantum qubit random robust systems tasks type