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Related papers: Algebraic measures of entanglement

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We provide a novel analysis of low-rank tensor completion based on hypergraph expanders. As a proxy for rank, we minimize the max-quasinorm of the tensor, which generalizes the max-norm for matrices. Our analysis is deterministic and shows…

Machine Learning · Statistics 2021-10-11 Kameron Decker Harris , Yizhe Zhu

The geometric measure of entanglement, which expresses the minimum distance to product states, has been generalized to distances to sets that remain invariant under the stochastic reducibility relation. For each such set, an associated…

Quantum Physics · Physics 2010-09-20 K. Uyanik , S. Turgut

We study the optimal transport problem for $d>2$ discrete measures. This is a linear programming problem on $d$-tensors. It gives a way to compute a "distance" between two sets of discrete measures. We introduce an entropic regularization…

Computer Vision and Pattern Recognition · Computer Science 2021-07-27 Shmuel Friedland

We study the rank one completion problem for tensors of arbitrary orders. The notion of rank one determinable tensors is introduced. We explore its properties and propose a recursive algorithm for computing rank one tensor completion. This…

Numerical Analysis · Mathematics 2026-04-28 Linghao Zhang , Ioana Dumitriu , Jiawang Nie

We construct a family of additive entanglement measures for pure multipartite states. The family is parametrised by a simplex and interpolates between the R\'enyi entropies of the one-particle reduced states and the recently-found universal…

Mathematical Physics · Physics 2023-08-02 Péter Vrana

We present a multipartite entanglement measure for $N$-qubit pure states, using the norm of the correlation tensor which occurs in the Bloch representation of the state. We compute this measure for several important classes of $N$-qubit…

Quantum Physics · Physics 2008-07-17 Ali Saif M. Hassan , Pramod S. Joag

In this paper, we show that the low rank matrix completion problem can be reduced to the problem of finding the rank of a certain tensor.

Optimization and Control · Mathematics 2013-07-24 Harm Derksen

We consider the mixed three-qubit bound entangled state defined as the normalized projector on the subspace that is complementary to an Unextendible Product Basis [C. H. Bennett et. al., Phys. Rev. Lett. 82, 5385 (1999)]. Using the fact…

Quantum Physics · Physics 2010-07-28 Cyril Branciard , Huangjun Zhu , Lin Chen , Valerio Scarani

The injective norm is a natural generalization to tensors of the operator norm of a matrix. In quantum information, the injective norm is one important measure of genuine multipartite entanglement of quantum states, where it is known as the…

Probability · Mathematics 2024-04-05 Stephane Dartois , Benjamin McKenna

Entanglement is a cornerstone in quantum information science, yet detecting it efficiently remains a challenging task. Focusing on non-positive partially transposed (NPT) states, we establish a hierarchy among entropy-based, majorization,…

Quantum Physics · Physics 2025-07-30 Akhil Kumar Awasthi , Sudipta Mondal , Rivu Gupta , Aditi Sen De

We construct an entanglement measure that coincides with the generalized concurrence for a general pure bipartite state based on wedge product. Moreover, we construct an entanglement measure for pure multi-qubit states, which are…

Quantum Physics · Physics 2007-05-23 Hoshang Heydari

The degree to which a pure quantum state is entangled can be characterized by the distance or angle to the nearest unentangled state. This geometric measure of entanglement, already present in a number of settings [A. Shimony, Ann. NY.…

Quantum Physics · Physics 2007-05-23 Tzu-Chieh Wei , Paul M. Goldbart

In the problem of entanglement there exist two different notions. One is the entanglement of a quantum state, characterizing the state structure. The other is entanglement production by quantum operators, describing the action of operators…

Quantum Physics · Physics 2019-05-22 V. I. Yukalov , E. P. Yukalova , V. A. Yurovsky

In quantum information theory, the Schmidt rank is a fundamental measure for the entanglement dimension of a pure bipartite state. Its natural definition uses the Schmidt decomposition of vectors on bipartite Hilbert spaces, which does not…

Quantum Physics · Physics 2024-06-21 Lauritz van Luijk , René Schwonnek , Alexander Stottmeister , Reinhard F. Werner

We introduce a protocol to classify three-qubit pure states into different entanglement classes and implement it on an NMR quantum processor. The protocol is designed in such a way that the experiments performed to classify the states can…

Quantum Physics · Physics 2024-11-07 Vaishali Gulati , Arvind , Kavita Dorai

The orthogonal decomposition factorizes a tensor into a sum of an orthogonal list of rankone tensors. We present several properties of orthogonal rank. We find that a subtensor may have a larger orthogonal rank than the whole tensor and…

Numerical Analysis · Mathematics 2022-12-05 Chao Zeng

We derive explicit bounds for the average entropy characterizing measurements of a pure quantum state of size $N$ in $L$ orthogonal bases. Lower bounds lead to novel entropic uncertainty relations, while upper bounds allow us to formulate…

We investigate the computational complexity of tensor rank, a concept that plays fundamental role in different topics of modern applied mathematics. For tensors over any integral domain, we prove that the rank problem is polynomial time…

Combinatorics · Mathematics 2016-11-08 Yaroslav Shitov

In this paper we study typical ranks of real $m\times n \times \ell$ tensors. In the case $ (m-1)(n-1)+1 \leq \ell \leq mn$ the typical ranks are contained in $\{\ell, \ell +1\}$, and $\ell$ is always a typical rank. We provide a geometric…

Algebraic Geometry · Mathematics 2024-07-12 Paul Breiding , Sarah Eggleston , Andrea Rosana

In this article the notion of the nondecreasing (ND) rank of a matrix or tensor is introduced. A tensor has an ND rank of r if it can be represented as a sum of r outer products of vectors, with each vector satisfying a monotonicity…

Machine Learning · Statistics 2025-10-21 Andrew McCormack