2017/07/31 by Timothy Proctor, Timothy J. Proctor, P. A. Knott +3 · 8 citations
Computer Science · Mathematics · Physics and Astronomy · #Algorithm #Computer science #Data mining #Estimation #Limit (mathematics) #Mathematical analysis #Mathematics #Measure (data warehouse) #Nonlinear system #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Mechanics and Applications #Quantum entanglement #Quantum limit #Quantum mechanics #Quantum network #Quantum sensor #Quantum state #Separable space #Set (abstract data type) #Statistical physics #quant-ph
paper · pdf · doi:10.1103/physrevlett.120.080501
published as Phys. Rev. Lett. 120, 080501 (2018) · Accepted to Phys. Rev. Lett. Changes in v2: Examples added to demonstrate the practical relevance of our results; general minor improvements. This letter is an updated, improved, and succinct version of our unpublished pre-print ArXiv:1702.04271. That paper will be updated at a later date to be a detailed follow-on to this letter
arxiv created 2017/12/04 · openalex publication_date 2018/02/21 · arxiv updated 2018/02/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We introduce a general model for a network of quantum sensors, and we use this model to consider the following question: When can entanglement between the sensors, and/or global measurements, enhance the precision with which the network can measure a set of unknown parameters? We rigorously answer this question by presenting precise theorems proving that for a broad class of problems there is, at most, a very limited intrinsic advantage to using entangled states or global measurements. Moreover, for many estimation problems separable states and local measurements are optimal, and can achieve the ultimate quantum limit on the estimation uncertainty. This immediately implies that there are broad conditions under which simultaneous estimation of multiple parameters cannot outperform individual, independent estimations. Our results apply to any situation in which spatially localized sensors are unitarily encoded with independent parameters, such as when estimating multiple linear or nonlinear optical phase shifts in quantum imaging, or when mapping out the spatial profile of an unknown magnetic field. We conclude by showing that entangling the sensors can enhance the estimation precision when the parameters of interest are global properties of the entire network.