Quantum key distribution rates from semidefinite programming

نویسندگان

چکیده

Computing the key rate in quantum distribution (QKD) protocols is a long standing challenge. Analytical methods are limited to handful of with highly symmetric measurement bases. Numerical can handle arbitrary bases, but either use min-entropy, which gives loose lower bound von Neumann entropy, or rely on cumbersome dedicated algorithms. Based recently discovered semidefinite programming (SDP) hierarchy converging conditional used for computing asymptotic rates device independent case, we introduce an SDP that converges secret case characterised devices. The resulting algorithm efficient, easy implement and use. We illustrate its performance by recovering known bounds extending high-dimensional QKD previously intractable cases. also it reanalyse experimental data demonstrate how higher be achieved when full statistics taken into account.

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Designing optimal quantum detectors via semidefinite programming

We consider the problem of designing an optimal quantum detector to minimize the probability of a detection error when distinguishing between a collection of quantum states, represented by a set of density operators. We show that the design of the optimal detector can be formulated as a semidefinite programming problem. Based on this formulation, we derive a set of necessary and sufficient cond...

متن کامل

Finite quantum tomography via semidefinite programming

Using the the convex semidefinite programming method and superoperator formalism we obtain the finite quantum tomography of some mixed quantum states such as: qudit tomography, N-qubit tomography, phase tomography and coherent spin state tomography, where that obtained results are in agreement with those of References [21, 24, 25, 4, 26].

متن کامل

Optimum quantum error recovery using semidefinite programming

Andrew S. Fletcher,* Peter W. Shor, and Moe Z. Win Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, Massachusetts 02420 Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA Received 7 June 2006; publishe...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Quantum

سال: 2023

ISSN: ['2521-327X']

DOI: https://doi.org/10.22331/q-2023-05-24-1019