Scalable protocol to coherence estimation from scarce data: Theory and experiment
Qi-Ming Ding, Ting Zhang, Hui Li, Da-Jian Zhang
TL;DR
The paper tackles the challenge of estimating quantum coherence from limited data by reframing the REC estimation into a tractable optimization via a relaxed objective that yields a computable lower bound. The core idea is to replace the NP-hard entropy minimization with a dual, gradient-enabled formulation that produces a lower bound $\beta$ on $C_r(\rho)$, remaining largely insensitive to system size. Numerical results show iteration complexity is size-agnostic, while experiments on two-qubit Werner states demonstrate reliable coherence estimates from scarce measurements. This approach enables scalable, non-tomographic coherence certification for large quantum systems and may extend to other quantum resources like entanglement.
Abstract
Key quantum features like coherence are the fundamental resources enabling quantum advantages and ascertaining their presence in quantum systems is crucial for developing quantum technologies. This task, however, faces severe challenges in the noisy intermediate-scale quantum era. On one hand, experimental data are typically scarce, rendering full state reconstruction infeasible. On the other hand, these features are usually quantified by highly nonlinear functionals that elude efficient estimations via existing methods. In this work, we propose a scalable protocol for estimating coherence from scarce data and further experimentally demonstrate its practical utility. The key innovation here is to relax the potentially NP-hard coherence estimation problem into a computationally efficient optimization. This renders the computational cost in our protocol insensitive to the system size, in sharp contrast to the exponential growth in traditional methods. This work opens a novel route toward estimating coherence of large-scale quantum systems under data-scarce conditions.
