Verification of entangled states under noisy measurements
Lan Zhang, Yinfei Li, Ye-Chao Liu, Jiangwei Shang
TL;DR
This work addresses verifying entangled quantum states under noisy measurement readouts. It develops a systematic noisy QSV framework, establishes a necessary and sufficient distinguishability condition for uniquely identifying the target state, and introduces a symmetric hypothesis-testing verification algorithm. The authors provide both analytical and SDP-based methods to quantify noise effects, demonstrating a nearly $N \sim \epsilon^{-2}$ scaling of the required sample size under symmetric testing and extending verification feasibility to GHZ, stabilizer, and W states in noisy scenarios. The results offer practical, robust verification protocols suitable for current NISQ experiments, enabling reliable entanglement verification with imperfect devices.
Abstract
Entanglement plays an indispensable role in numerous quantum information and quantum computation tasks, underscoring the need for efficiently verifying entangled states. In recent years, quantum state verification has received increasing attention, yet the challenge of addressing noise effects in implementing this approach remains unsolved. In this work, we provide a systematic assessment of the performance of quantum state verification protocols in the presence of measurement noise. Based on the analysis, a necessary and sufficient condition is provided to uniquely identify the target state under noisy measurements. Moreover, we propose a symmetric hypothesis testing verification algorithm with noisy measurements. Subsequently, using a noisy nonadaptive verification strategy of GHZ and stabilizer states, the noise effects on the verification efficiency are illustrated. From both analytical and numerical perspectives, we demonstrate that the noisy verification protocol exhibits a negative quadratic relationship between the sample complexity and the infidelity. Our method can be easily applied to real experimental settings, thereby demonstrating its promising prospects.
