From the 1 of 9 linked papers with an AI index.
6 papers · 1 filter
The log log jam in Gaussian state tomography
Sitan Chen, Weiyuan Gong, Qi Ye +1
The paper proves that any tomography protocol using Gaussian measurements on continuous‑variable systems inevitably incurs a sample complexity that scales as log log E with the sys…
Adaptivity can help exponentially for shadow tomography
Sitan Chen, Weiyuan Gong, Zhihan Zhang
In recent years there has been significant interest in understanding the statistical complexity of learning from quantum data under the constraint that one can only make unentangle…
Stabilizer bootstrapping: A recipe for efficient agnostic tomography and magic estimation
Sitan Chen, Weiyuan Gong, Qi Ye +1
We study the task of agnostic tomography: given copies of an unknown -qubit state which has fidelity with some state in a given class , find a state which has fidel…
Optimal tradeoffs for estimating Pauli observables
Sitan Chen, Weiyuan Gong, Qi Ye
We revisit the problem of Pauli shadow tomography: given copies of an unknown -qubit quantum state , estimate for some set of Pauli operators to within…
Predicting quantum channels over general product distributions
Sitan Chen, Jaume de Dios Pont, Jun-Ting Hsieh +3
We investigate the problem of predicting the output behavior of unknown quantum channels. Given query access to an -qubit channel and an observable , we aim to learn the…
Optimal high-precision shadow estimation
Sitan Chen, Jerry Li, Allen Liu
We give the first tight sample complexity bounds for shadow tomography and classical shadows in the regime where the target error is below some sufficiently small inverse polynomia…