Genuine Multipartite Nonlocality sharing under sequential measurement
Sk Sahadat Hossain, Indrani Chattopadhyay

TL;DR
This paper investigates how genuine multipartite nonlocality can be shared among multiple observers in $n$-qubit GHZ states using unbiased unsharp measurements, revealing limits on sharing in both unilateral and multilateral scenarios.
Contribution
It extends nonlocality sharing analysis to $n$-qubit GHZ states under sequential measurements, providing new bounds and strategies for sharing nonlocality.
Findings
Maximum two copies of an observer can share nonlocality in four-qubit GHZ state
Multilateral sharing does not surpass unilateral sharing in the studied scenarios
Unsharp measurements are crucial for optimizing nonlocality sharing
Abstract
The study of quantum nonlocality sharing has garnered significant attention, particularly for two-qubit and three-qubit entangled systems. In this paper, we extend the investigation to -qubit Greenberger-Horne-Zeilinger (GHZ) systems, analyzing nonlocality sharing under unbiased unsharp measurements. Employing the Seevink and Svetlichny inequalities, we explore both unilateral and multilateral sequential measurement scenarios. In the unilateral scenario, we derive the range for which an observer's multiple copies can share genuine -partite nonlocality with single copies of the remaining parties. In the multilateral scenario, we identify the maximum number of independent observers on sides who can share genuine -partite nonlocality with other parties. A crucial aspect of our results is that all findings stem from a measurement strategy where each sequential observer utilizes…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDistributed Sensor Networks and Detection Algorithms · Advanced Research in Systems and Signal Processing
