Antonios Varvitsiotis
National University of Singapore
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Publication
Featured researches published by Antonios Varvitsiotis.
Physical Review Letters | 2016
Jamie Sikora; Antonios Varvitsiotis; Zhaohui Wei
Consider a two-party correlation that can be generated by performing local measurements on a bipartite quantum system. A question of fundamental importance is to understand how many resources, which we quantify by the dimension of the underlying quantum system, are needed to reproduce this correlation. In this Letter, we identify an easy-to-compute lower bound on the smallest Hilbert space dimension needed to generate a given two-party quantum correlation. We show that our bound is tight on many well-known correlations and discuss how it can rule out correlations of having a finite-dimensional quantum representation. We show that our bound is multiplicative under product correlations and also that it can witness the nonconvexity of certain restricted-dimensional quantum correlations.
Mathematical Programming | 2018
Anupam Prakash; Jamie Sikora; Antonios Varvitsiotis; Zhaohui Wei
An
Discrete and Computational Geometry | 2017
Chris D. Godsil; David E. Roberson; Brendan Rooney; Robert Šámal; Antonios Varvitsiotis
Electronic Notes in Discrete Mathematics | 2015
Chris D. Godsil; David E. Roberson; Brendan Rooney; Robert Šámal; Antonios Varvitsiotis
n\times n
Physical Review A | 2016
Jamie Sikora; Antonios Varvitsiotis; Zhaohui Wei
arXiv: Quantum Physics | 2016
Albert Atserias; Laura Mancinska; David E. Roberson; Robert Šámal; Simone Severini; Antonios Varvitsiotis
n×n matrix X is called completely positive semidefinite (cpsd) if there exist
Archive | 2017
Anupam Prakash; Antonios Varvitsiotis
Chicago Journal of Theoretical Computer Science | 2015
Laura Mancinska; David E. Roberson; Antonios Varvitsiotis
d\times d
arXiv: Quantum Physics | 2018
Yukun Wang; Ignatius William Primaatmaja; Emilien Lavie; Antonios Varvitsiotis; Charles Ci Wen Lim
arXiv: Quantum Physics | 2018
Le Phuc Thinh; Antonios Varvitsiotis; Yu Cai
d×d Hermitian positive semidefinite matrices