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Quantum key distribution rates from semidefinite programming

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Mateus Araújo1, Marcus Huber1,2, Miguel Navascués1, Matej Pivoluska2,3,4, and Armin Tavakoli1,2

1Institute for Quantum Optics and Quantum Information (IQOQI), Austrian Academy of Sciences, Boltzmanngasse 3, 1090 Vienna, Austria
2Vienna Center for Quantum Science and Technology, Atominstitut, TU Wien, 1020 Vienna, Austria
3Institute of Computer Science, Masaryk University, 602 00 Brno, Czech Republic
4Institute of Physics, Slovak Academy of Sciences, 845 11 Bratislava, Slovakia

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Abstract

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

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Cited by

[1] Simon Morelli, Marcus Huber, and Armin Tavakoli, “Resource-efficient high-dimensional entanglement detection via symmetric projections”, arXiv:2304.04274, (2023).

[2] Martin Sandfuchs, Marcus Haberland, V. Vilasini, and Ramona Wolf, “Security of differential phase shift QKD from relativistic principles”, arXiv:2301.11340, (2023).

[3] Oisín Faust and Hamza Fawzi, “Rational approximations of operator monotone and operator convex functions”, arXiv:2305.12405, (2023).

The above citations are from SAO/NASA ADS (last updated successfully 2023-05-24 23:13:54). The list may be incomplete as not all publishers provide suitable and complete citation data.

On Crossref’s cited-by service no data on citing works was found (last attempt 2023-05-24 23:13:52).

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