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Semidefinite programming (SDP) is a unifying framework that generalizes both linear programming and quadratically-constrained quadratic programming, while also yielding efficient solvers, both in theory and in practice. However, there exist…

Data Structures and Algorithms · Computer Science 2022-10-24 Elena Grigorescu , Young-San Lin , Sandeep Silwal , Maoyuan Song , Samson Zhou

Quantum steering, an intermediate quantum correlation lying between entanglement and nonlocality, has emerged as a critical quantum resource for a variety of quantum information processing tasks such as quantum key distribution and true…

Quantum Physics · Physics 2025-01-22 Yansa Lu , Zhihua Chen , Zhihao Ma , Shao-Ming Fei

We introduce SDPB: an open-source, parallelized, arbitrary-precision semidefinite program solver, designed for the conformal bootstrap. SDPB significantly outperforms less specialized solvers and should enable many new computations. As an…

High Energy Physics - Theory · Physics 2015-02-10 David Simmons-Duffin

We present two sets of computable entanglement measures for multipartite systems where each subsystem can have different degrees of freedom (so-called qudits). One set, called 'separability' measure, reveals which of the subsystems are…

Quantum Physics · Physics 2009-06-10 Beatrix C. Hiesmayr , Marcus Huber , Philipp Krammer

In this paper we consider the problem of unambiguous discrimination between a set of linearly independent pure quantum states. We show that the design of the optimal measurement that minimizes the probability of an inconclusive result can…

Quantum Physics · Physics 2016-11-17 Yonina C. Eldar

We study and extend the semidefinite programming (SDP) hierarchies introduced in [Phys. Rev. Lett. 115, 020501] for the characterization of the statistical correlations arising from finite dimensional quantum systems. First, we introduce…

Quantum Physics · Physics 2015-10-28 Miguel Navascues , Adrien Feix , Mateus Araujo , Tamas Vertesi

Semidefinite Programming (SDP) provides tight lower bounds for Optimal Power Flow problems. However, solving large-scale SDP problems requires exploiting sparsity. In this paper, we experiment several clique decomposition algorithms that…

Optimization and Control · Mathematics 2019-12-20 Julie Sliwak , Miguel Anjos , Lucas Létocart , Jean Maeght , Emiliano Traversi

The measured relative entropies of quantum states and channels find operational significance in quantum information theory as achievable error rates in hypothesis testing tasks. They are of interest in the near term, as they correspond to…

Quantum Physics · Physics 2025-10-21 Zixin Huang , Mark M. Wilde

This paper investigates the minimization of the expectation of piecewise polynomial loss functions over Wasserstein balls. This optimization problem often appears as a key sub-problem of distributionally robust optimization problems. We…

Optimization and Control · Mathematics 2026-02-25 N. D. Dizon , Q. Y. Huang , T. D. Chuong , G. Li , V. Jeyakumar

The framework of Integral Quadratic Constraints of Lessard et al. (2014) reduces the computation of upper bounds on the convergence rate of several optimization algorithms to semi-definite programming (SDP). Followup work by Nishihara et…

Machine Learning · Statistics 2018-03-06 Guilherme França , José Bento

The problem of determining whether a given quantum state is entangled lies at the heart of quantum information processing, which is known to be an NP-hard problem in general. Despite the proposed many methods such as the positive partial…

Quantum Physics · Physics 2018-07-18 Sirui Lu , Shilin Huang , Keren Li , Jun Li , Jianxin Chen , Dawei Lu , Zhengfeng Ji , Yi Shen , Duanlu Zhou , Bei Zeng

Large-scale quantum-correlated networks could transform technologies ranging from communications and cryptography to computation, metrology, and simulation of novel materials. Critical to achieving such quantum enhancements is distributing…

Entanglement distillation is a basic task in quantum information, and the distillable entanglement of three bipartite reduced density matrices from a tripartite pure state has been studied in [Phys. Rev. A 84, 012325 (2011)]. We extend this…

Quantum Physics · Physics 2019-09-09 Yize Sun , Lin Chen

Entangled states with a positive partial transpose (so-called PPT states) are central to many interesting problems in quantum theory. On one hand, they are considered to be weakly entangled, since no pure state entanglement can be distilled…

Quantum Physics · Physics 2019-07-17 Károly F. Pál , Tamás Vértesi

The logarithmic negativity of a bipartite quantum state is a widely employed entanglement measure in quantum information theory, due to the fact that it is easy to compute and serves as an upper bound on distillable entanglement. More…

Quantum Physics · Physics 2020-09-30 Xin Wang , Mark M. Wilde

Measures of entanglement can be employed for the analysis of numerous quantum information protocols. Due to computational convenience, logarithmic negativity is often the choice in the case of continuous variable systems. In this work, we…

Bound entanglement is a special form of quantum entanglement that cannot be used for distillation, i.e., the local transformation of copies of arbitrarily entangled states into a smaller number of approximately maximally entangled states.…

Quantum Physics · Physics 2025-08-01 Beatrix C. Hiesmayr , Christopher Popp , Tobias C. Sutter

Employing a recently proposed separability criterion we develop analytical lower bounds for the concurrence and for the entanglement of formation of bipartite quantum systems. The separability criterion is based on a nondecomposable…

Quantum Physics · Physics 2007-05-23 Heinz-Peter Breuer

The unique features of entanglement and non-locality in quantum systems, where there are pairs of bipartite states perfectly distinguishable by general entangled measurements yet indistinguishable by local operations and classical…

Quantum Physics · Physics 2024-11-19 Chenghong Zhu , Chengkai Zhu , Zhiping Liu , Xin Wang

This paper develops a new storage-optimal algorithm that provably solves generic semidefinite programs (SDPs) in standard form. This method is particularly effective for weakly constrained SDPs. The key idea is to formulate an approximate…

Optimization and Control · Mathematics 2020-06-19 Lijun Ding , Alp Yurtsever , Volkan Cevher , Joel A. Tropp , Madeleine Udell
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