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This paper introduces a new robust interior point method analysis for semidefinite programming (SDP). This new robust analysis can be combined with either logarithmic barrier or hybrid barrier. Under this new framework, we can improve the…

Optimization and Control · Mathematics 2021-11-22 Baihe Huang , Shunhua Jiang , Zhao Song , Runzhou Tao , Ruizhe Zhang

Quantum low density parity check (QLDPC) codes are useful primitives for quantum information processing because they can be encoded and decoded efficiently. Besides, the error correcting capability of a few QLDPC codes exceeds the quantum…

Quantum Physics · Physics 2008-07-15 K. H. Ho , H. F. Chau

Semidefinite programs (SDPs) are standard convex problems that are frequently found in control and optimization applications. Interior-point methods can solve SDPs in polynomial time up to arbitrary accuracy, but scale poorly as the size of…

Optimization and Control · Mathematics 2022-01-10 Jared Miller , Yang Zheng , Mario Sznaier , Antonis Papachristodoulou

For a large class of optimization problems, namely those that can be expressed as finite-valued constraint satisfaction problems (VCSPs), we establish a dichotomy on the number of levels of the Lasserre hierarchy of semi-definite programs…

Logic in Computer Science · Computer Science 2016-09-27 Anuj Dawar , Pengming Wang

Entanglement is a striking feature of quantum mechanics, and it has a key property called unextendibility. In this paper, we present a framework for quantifying and investigating the unextendibility of general bipartite quantum states.…

Quantum Physics · Physics 2024-03-28 Kun Wang , Xin Wang , Mark M. Wilde

Quantum information theory is plagued by the problem of regularisations, which require the evaluation of formidable asymptotic quantities. This makes it computationally intractable to gain a precise quantitative understanding of the…

Quantum Physics · Physics 2025-03-11 Ludovico Lami , Francesco Anna Mele , Bartosz Regula

This paper develops new semidefinite programming (SDP) relaxation techniques for two classes of mixed binary quadratically constrained quadratic programs (MBQCQP) and analyzes their approximation performance. The first class of problem…

Optimization and Control · Mathematics 2014-03-18 Zi Xu , Mingyi Hong

We present a stronger version of the Doherty-Parrilo-Spedalieri (DPS) hierarchy of approximations for the set of separable states. Unlike DPS, our hierarchy converges exactly at a finite number of rounds for any fixed input dimension. This…

Quantum Physics · Physics 2017-06-19 Aram W. Harrow , Anand Natarajan , Xiaodi Wu

A fundamental problem in quantum coding theory is to determine the maximum size of quantum codes of given block length and distance. A recent work introduced bounds based on semidefinite programming, strengthening the well-known quantum…

Quantum Physics · Physics 2026-03-23 Gerard Anglès Munné , Felix Huber

Bound entanglement with a nonpositive partial transposition (NPT) does not exist. For any NPT entangled state a distillation procedure can be based on a certain number of copies. This number is the minimal Schmidt rank of a pure state…

Quantum Physics · Physics 2009-11-02 J. Sperling , W. Vogel

In this paper, we present a new entanglement monotone for bipartite quantum states. Its definition is inspired by the so-called intrinsic information of classical cryptography and is given by the halved minimum quantum conditional mutual…

Quantum Physics · Physics 2015-06-26 Matthias Christandl , Andreas Winter

We analyze entanglement swapping (ES) of partially entangled pure states with arbitrary Schmidt rank from the perspective of quantum state discrimination. It is shown that the standard deterministic ES protocol is related with an optimal…

Quantum Physics · Physics 2014-02-07 M. A. Solís-Prosser , A. Delgado , O. Jiménez , L. Neves

In the last years many results in the area of semidefinite programming were obtained for invariant (finite dimensional, or infinite dimensional) semidefinite programs - SDPs which have symmetry. This was done for a variety of problems and…

Optimization and Control · Mathematics 2019-11-07 Christine Bachoc , Dion C. Gijswijt , Alexander Schrijver , Frank Vallentin

We discuss the problem of characterizing upper bounds on entanglement in a bipartite quantum system when only the reduced density matrices (marginals) are known. In particular, starting from the known two-qubit case, we propose a family of…

Seeking tighter relaxations of combinatorial optimization problems, semidefinite programming is a generalization of linear programming that offers better bounds and is still polynomially solvable. Yet, in practice, a semidefinite program is…

Optimization and Control · Mathematics 2023-11-17 Daniel Porumbel

The Schmidt coefficients capture all entanglement properties of a pure bipartite state and therefore determine its usefulness for quantum information processing. While the quantification of the corresponding properties in mixed states is…

Quantum Physics · Physics 2019-04-08 Gael Sentís , Christopher Eltschka , Otfried Gühne , Marcus Huber , Jens Siewert

Distillable entanglement ($E_d$) is one of the acceptable measures of entanglement of mixed states. Based on discrimination through local operation and classical communication, this paper gives $E_d$ for two classes of orthogonal…

Quantum Physics · Physics 2009-11-10 S. Hamieh , H. Zaraket

In this paper, we provide a complete mathematical theory for the entanglement of mixtures of Dicke states. These quantum states form an important subclass of bosonic states arising in the study of indistinguishable particles. We introduce a…

Quantum Physics · Physics 2026-02-18 Aabhas Gulati , Ion Nechita , Clément Pellegrini

Quantum state discrimination is a fundamental primitive in quantum information processing, underpinning tasks in quantum communication, sensing, and learning. We consider the general Bayes framework, as introduced by Helstrom, for state…

Quantum Physics · Physics 2026-05-01 Ankith Mohan , Jamie Sikora , Sarvagya Upadhyay

We give two quantum algorithms for solving semidefinite programs (SDPs) providing quantum speed-ups. We consider SDP instances with $m$ constraint matrices, each of dimension $n$, rank at most $r$, and sparsity $s$. The first algorithm…