scispace - formally typeset
Open AccessProceedings ArticleDOI

Classical deterministic complexity of Edmonds' Problem and quantum entanglement

Reads0
Chats0
TLDR
It turns out that one of the most important cases when Edmonds' problem can be solved in polynomial deterministic time, i.e. an intersection of two geometric matroids, corresponds to unentangled (aka separable) bipartite density matrices.
Abstract
Generalizing a decision problem for bipartite perfect matching, J. Edmonds introduced in [14] the problem (now known as the Edmonds Problem) of deciding if a given linear subspace of M(N) contains a nonsingular matrix, where M(N) stands for the linear space of complex NxN matrices. This problem led to many fundamental developments in matroid theory etc.Classical matching theory can be defined in terms of matrices with nonnegative entries. The notion of Positive operator, central in Quantum Theory, is a natural generalization of matrices with nonnegative entries. (Here operator refers to maps from matrices to matrices.) First, we reformulate the Edmonds Problem in terms of of completely positive operators, or equivalently, in terms of bipartite density matrices. It turns out that one of the most important cases when Edmonds' problem can be solved in polynomial deterministic time, i.e. an intersection of two geometric matroids, corresponds to unentangled (aka separable) bipartite density matrices. We introduce a very general class (or promise) of linear subspaces of M(N) on which there exists a polynomial deterministic time algorithm to solve Edmonds' problem. The algorithm is a thoroughgoing generalization of algorithms in [23], [26], and its analysis benefits from an operator analog of permanents, so called Quantum Permanents. Finally, we prove that the weak membership problem for the convex set of separable normalized bipartite density matrices is NP-HARD.

read more

Citations
More filters
Journal ArticleDOI

Entanglement detection

TL;DR: In this article, the basic elements of entanglement theory for two or more particles and verification procedures, such as Bell inequalities, entangle witnesses, and spin squeezing inequalities, are discussed.
Book

The Theory of Quantum Information

TL;DR: In this article, the authors present a self-contained book on the theory of quantum information focusing on precise mathematical formulations and proofs of fundamental facts that form the foundation of the subject.

Geometry of Quantum States

TL;DR: In this article, the space of isospectral 0Hermitian matrices is shown to be the space in which the number 6) and 7) occur twice in the figure, and the discussion between eqs.(5.14) and (5.15) is incorrect.
Posted Content

Most tensor problems are NP-hard

TL;DR: It is proved that multilinear (tensor) analogues of many efficiently computable problems in numerical linear algebra are NP-hard and how computing the combinatorial hyperdeterminant is NP-, #P-, and VNP-hard.
References
More filters
Book

Convex bodies : the Brunn-Minkowski theory

TL;DR: Inequalities for mixed volumes 7. Selected applications Appendix as discussed by the authors ] is a survey of mixed volumes with bounding boxes and quermass integrals, as well as a discussion of their applications.
Book

Interior-Point Polynomial Algorithms in Convex Programming

TL;DR: This book describes the first unified theory of polynomial-time interior-point methods, and describes several of the new algorithms described, e.g., the projective method, which have been implemented, tested on "real world" problems, and found to be extremely efficient in practice.
Book

Geometric Algorithms and Combinatorial Optimization

TL;DR: In this article, the Fulkerson Prize was won by the Mathematical Programming Society and the American Mathematical Society for proving polynomial time solvability of problems in convexity theory, geometry, and combinatorial optimization.
Journal ArticleDOI

The complexity of computing the permanent

TL;DR: It is shown that the permanent function of (0, 1)-matrices is a complete problem for the class of counting problems associated with nondeterministic polynomial time computations.
Journal ArticleDOI

Robust Convex Optimization

TL;DR: If U is an ellipsoidal uncertainty set, then for some of the most important generic convex optimization problems (linear programming, quadratically constrained programming, semidefinite programming and others) the corresponding robust convex program is either exactly, or approximately, a tractable problem which lends itself to efficientalgorithms such as polynomial time interior point methods.
Related Papers (5)
Trending Questions (1)
What are the downside of deterministic property in classical computer?

The text does not provide information about the downsides of the deterministic property in classical computers.