Unbounded sequential multipartite nonlocality via violation of Mermin inequality
Bang-Zhu Shen, Mao-Sheng Li

TL;DR
This paper demonstrates that in multi-party quantum systems, unbounded sequential observers can detect nonlocality via Mermin inequality violations, extending previous bipartite and tripartite studies to more complex configurations.
Contribution
It extends the understanding of sequential nonlocality detection to multi-party systems with multiple observer chains, surpassing prior bipartite limitations.
Findings
Unbounded sequential observers can violate Mermin inequality in n-partite systems.
Sequential nonlocality detection is possible in both single-chain and double-chain scenarios.
Increasing subsystems enables more observers to detect nonlocality simultaneously.
Abstract
Quantum nonlocality is a significant feature in quantum information theory, prompting recent investigations into the potential reuse of post-measurement states to uncover nonlocality among sequentially measuring observers. While prior studies primarily focused on bipartite or tripartite systems and observers with one chain, such as multiple Bobs with a single Alice or multiple Charlies with a single Alice and Bob, our work extends beyond this framework. We explore sequential nonlocality in systems comprising more parties and observer chains. Our findings reveal that in -partite systems, regardless of whether it is a single-chain or double-chain scenario, there exist unbounded sequential observers capable of detecting nonlocality through violations of the Mermin inequality. In contrast to the conjecture that sequential Bell nonlocality cannot manifest with multiple Alices and Bobs in…
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
TopicsMathematical Inequalities and Applications · Probability and Risk Models · Random Matrices and Applications
