Global vs. Local Discrimination of Locally Implementable Multipartite Unitaries
- URL: http://arxiv.org/abs/2509.10430v1
- Date: Fri, 12 Sep 2025 17:36:23 GMT
- Title: Global vs. Local Discrimination of Locally Implementable Multipartite Unitaries
- Authors: Satyaki Manna, Sneha Suresh, Anandamay Das Bhowmik, Debashis Saha,
- Abstract summary: We study single-shot distinguishability of locally implementable multipartite unitaries under Local Operations and Classical Communication (LOCC) and global operations.<n>Our findings uncover three surprising features in the bipartite setting and establish new structural limits for unitary discrimination.
- Score: 0.5872014229110214
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We study single-shot distinguishability of locally implementable multipartite unitaries under Local Operations and Classical Communication (LOCC) and global operations. As unitary discrimination depends on both the choice of probing states and the measurements on the evolved states, we classify LOCC and global distinguishability into two categories: adaptive strategies, where probing states are chosen based on measurement outcomes from other subsystems, and restricted strategies, where probing states remain fixed. Our findings uncover three surprising features in the bipartite setting and establish new structural limits for unitary discrimination: (i) Certain pairs of unitaries are globally distinguishable with restricted strategies but indistinguishable under LOCC, even with adaptive strategies. (ii) There exist sets of four unitaries that are distinguishable via LOCC, yet remain globally indistinguishable with restricted strategies. (iii) Some sets of unitaries are globally indistinguishable under adaptive strategies, when probed with separable states, but become distinguishable via LOCC.
Related papers
- AINet: Anchor Instances Learning for Regional Heterogeneity in Whole Slide Image [61.54860340942449]
We introduce a novel concept of anchor instance (AI), a compact subset of instances that are representative within their regions (local) and discriminative at the bag (global) level.<n>These AIs act as semantic references to guide interactions across regions, correcting non-discriminative patterns while preserving regional diversity.<n>We develop a concise yet effective framework, AINet, which employs a simple predictor and surpasses state-of-the-art methods with substantially fewer FLOPs and parameters.
arXiv Detail & Related papers (2026-02-21T09:36:27Z) - Global restrictions under local state discrimination [49.1574468325115]
Local distinguishability can restrict global properties of bi-partite states.<n>We show that optimal local state discrimination can become a powerful tool to limit global behaviours.
arXiv Detail & Related papers (2024-11-29T11:14:23Z) - Adaptive Global-Local Representation Learning and Selection for
Cross-Domain Facial Expression Recognition [54.334773598942775]
Domain shift poses a significant challenge in Cross-Domain Facial Expression Recognition (CD-FER)
We propose an Adaptive Global-Local Representation Learning and Selection framework.
arXiv Detail & Related papers (2024-01-20T02:21:41Z) - Discriminative Radial Domain Adaptation [62.22362756424971]
We propose Discriminative Radial Domain Adaptation (DRDR) which bridges source and target domains via a shared radial structure.
We show that transferring such an inherently discriminative structure would enable to enhance feature transferability and discriminability simultaneously.
Our method is shown to consistently outperforms state-of-the-art approaches on varied tasks.
arXiv Detail & Related papers (2023-01-01T10:56:31Z) - Global Meets Local: Effective Multi-Label Image Classification via
Category-Aware Weak Supervision [37.761378069277676]
This paper builds a unified framework to perform effective noisy-proposal suppression.
We develop a cross-granularity attention module to explore the complementary information between global and local features.
Our framework achieves superior performance over state-of-the-art methods.
arXiv Detail & Related papers (2022-11-23T05:39:17Z) - Enhancing Prototypical Few-Shot Learning by Leveraging the Local-Level
Strategy [75.63022284445945]
We find that the existing works often build their few-shot model based on the image-level feature by mixing all local-level features.
We present (a) a local-agnostic training strategy to avoid the discriminative location bias between the base and novel categories, and (b) a novel local-level similarity measure to capture the accurate comparison between local-level features.
arXiv Detail & Related papers (2021-11-08T08:45:15Z) - Unambiguous discrimination of Fermionic states through local operations
and classical communication [68.8204255655161]
The paper studies unambiguous discrimination of Fermionic states through local operations and classical communication (LOCC)
We show that it is not always possible to distinguish two Fermionic states through LOCC unambiguously with the same success probability as if global measurements were allowed.
arXiv Detail & Related papers (2020-09-11T21:08:52Z) - Discriminating bipartite mixed states by local operations [0.4724825031148411]
We show that the success probability of a global scheme for mixed-state discrimination can be achieved perfectly by the local scheme.
This simulation is perfect for local rather than global schemes due to the existence of entanglement and global coherence in the pure states.
arXiv Detail & Related papers (2020-03-13T04:50:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.