Mixed state entanglement from symmetric matrix inequalities
- URL: http://arxiv.org/abs/2502.18446v1
- Date: Tue, 25 Feb 2025 18:41:35 GMT
- Title: Mixed state entanglement from symmetric matrix inequalities
- Authors: Albert Rico,
- Abstract summary: We develop a new family of positive maps with further detection capabilities.<n>In the simplest case, we generalize the reduction map to detect more generic states using both multiple copies and local filters.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recently, a toolkit of highly symmetric techniques employing matrix inequalities has been developed to detect entanglement in various ways. Here we unifiedly explain in detail these methods, and expand them to a new family of positive maps with further detection capabilities. In the simplest case, we generalize the reduction map to detect more generic states using both multiple copies and local filters. Through the Choi-Jamio{\l}kowski isomorphism, this family of maps leads to a construction of multipartite entanglement witnesses. Discussions and examples are provided regarding the detection of states with local positive partial transposition and the use of multiple copies.
Related papers
- State-witness contraction [0.0]
We present a method to construct entanglement witnesses for arbitrarily large multipartite systems.<n>As a proof of principle we show that, using little shared quantum resources, the method allows to reuse witnesses unable to detect states with local positive partial transpositions into new ones able to do so.
arXiv Detail & Related papers (2025-02-24T22:44:16Z) - Detection of entanglement via moments of positive maps [0.1813006808606333]
We reexamined the moments of positive maps and the criterion based on these moments to detect entanglement.<n>For $2 otimes 4$ systems, we find that moments of reduction map are capable to detect a family of bound entangled states.<n>For three qubits system, we find that applying reduction map to one of the qubit is equivalent to partial transpose operation.
arXiv Detail & Related papers (2024-05-22T07:53:17Z) - Accelerated Discovery of Machine-Learned Symmetries: Deriving the
Exceptional Lie Groups G2, F4 and E6 [55.41644538483948]
This letter introduces two improved algorithms that significantly speed up the discovery of symmetry transformations.
Given the significant complexity of the exceptional Lie groups, our results demonstrate that this machine-learning method for discovering symmetries is completely general and can be applied to a wide variety of labeled datasets.
arXiv Detail & Related papers (2023-07-10T20:25:44Z) - Unextendibility, uncompletability, and many-copy indistinguishable ensembles [49.1574468325115]
We show that the complement of any bipartite pure entangled state is spanned by product states which form a nonorthogonal unextendible product basis (nUPB) of maximum cardinality.<n>We also report a class of multipartite many-copy indistinguishable ensembles for which local indistinguishability property increases with decreasing number of mixed states.
arXiv Detail & Related papers (2023-03-30T16:16:41Z) - Oracle-Preserving Latent Flows [58.720142291102135]
We develop a methodology for the simultaneous discovery of multiple nontrivial continuous symmetries across an entire labelled dataset.
The symmetry transformations and the corresponding generators are modeled with fully connected neural networks trained with a specially constructed loss function.
The two new elements in this work are the use of a reduced-dimensionality latent space and the generalization to transformations invariant with respect to high-dimensional oracles.
arXiv Detail & Related papers (2023-02-02T00:13:32Z) - The Ordered Matrix Dirichlet for Modeling Ordinal Dynamics [54.96229007229786]
We propose the Ordered Matrix Dirichlet (OMD) to map latent states to observed action types.
Models built on the OMD recover interpretable latent states and show superior forecasting performance in few-shot settings.
arXiv Detail & Related papers (2022-12-08T08:04:26Z) - Self-Supervised Training with Autoencoders for Visual Anomaly Detection [61.62861063776813]
We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold.
We adapt a self-supervised learning regime that exploits discriminative information during training but focuses on the submanifold of normal examples.
We achieve a new state-of-the-art result on the MVTec AD dataset -- a challenging benchmark for visual anomaly detection in the manufacturing domain.
arXiv Detail & Related papers (2022-06-23T14:16:30Z) - Positive maps from the walled Brauer algebra [4.4378250612684]
We present positive maps and matrix inequalities for variables from the positive cone.
Using our formalism, these maps can be obtained in a systematic and clear way.
arXiv Detail & Related papers (2021-12-23T17:42:45Z) - Diagonal unitary and orthogonal symmetries in quantum theory [1.5229257192293197]
We show that this class of matrices (and maps) encompasses a wide variety of scenarios, thereby unifying their study.
For linear maps, we provide explicit characterizations of the stated covariance in terms of their Kraus, Stinespring, and Choi representations.
We also describe the invariant subspaces of these maps and use their structure to provide necessary and sufficient conditions for separability of the associated invariant bipartite states.
arXiv Detail & Related papers (2020-10-15T17:25:38Z) - Positive maps and trace polynomials from the symmetric group [0.0]
We develop a method to obtain operator inequalities and identities in several variables.
We give connections to concepts in quantum information theory and invariant theory.
arXiv Detail & Related papers (2020-02-28T17:43:37Z) - Optimal Iterative Sketching with the Subsampled Randomized Hadamard
Transform [64.90148466525754]
We study the performance of iterative sketching for least-squares problems.
We show that the convergence rate for Haar and randomized Hadamard matrices are identical, andally improve upon random projections.
These techniques may be applied to other algorithms that employ randomized dimension reduction.
arXiv Detail & Related papers (2020-02-03T16:17:50Z)
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.