Quantum advantages for Pauli channel estimation
Senrui Chen, Sisi Zhou, Alireza Seif, Liang Jiang
Abstract
We show that entangled measurements provide an exponential advantage in sample complexity for Pauli channel estimation, which is both a fundamental problem and a practically important subroutine for benchmarking near-term quantum devices. The specific task we consider is to simultaneously learn all the eigenvalues of an $n$-qubit Pauli channel to $\ifmmode\pm\else\textpm\fi{}\ensuremath{\varepsilon}$ precision. We give an estimation protocol with an $n$-qubit ancilla that succeeds with high probability using only $O(n/{\ensuremath{\varepsilon}}^{2})$ copies of the Pauli channel, while proving that any ancilla-free protocol (possibly with adaptive control and channel concatenation) would need at least $\mathrm{\ensuremath{\Omega}}({2}^{n/3})$ rounds of measurement. We further study the advantages provided by a small number of ancillas. For the case that a $k$-qubit ancilla ($k\ensuremath{\le}n$) is available, we obtain a sample complexity lower bound of $\mathrm{\ensuremath{\Omega}}({2}^{(n\ensuremath{-}k)/3})$ for any nonconcatenating protocol, and a stronger lower bound of $\mathrm{\ensuremath{\Omega}}(n{2}^{n\ensuremath{-}k})$ for any nonadaptive, nonconcatenating protocol, which is shown to be tight. We also show how to apply the ancilla-assisted estimation protocol to a practical quantum benchmarking task in a noise-resilient and sample-efficient manner, given reasonable noise assumptions. Our results provide a practically interesting example for quantum advantages in learning and also bring insights for quantum benchmarking.