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High-dimensional quantum key distribution rates for multiple measurement bases

2 Pith papers cite this work. Polarity classification is still indexing.

2 Pith papers citing it
abstract

We investigate the advantages of high-dimensional encoding for a quantum key distribution protocol. In particular, we address a BBM92-like protocol where the dimension of the systems can be larger than two and more than two mutually unbiased bases (MUBs) can be employed. Indeed, it is known that, for a system whose dimension $d$ is a prime or the power of a prime, up to $d+1$ MUBs can be found. We derive an analytic expression for the asymptotic key rate when $d+1$ MUBs are exploited and show the effects of using different numbers of MUBs on the performance of the protocol. Then, we move to the non-asymptotic case and optimize the finite key rate against collective and coherent attacks for generic dimension of the systems and all possible numbers of MUBs. In the finite-key scenario, we find that, if the number of rounds is small enough, the highest key rate is obtained by exploiting three MUBs, instead of $d+1$ as one may expect.

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quant-ph 2

years

2026 1 2025 1

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UNVERDICTED 2

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representative citing papers

Quantum Uncertainty and Entropy

quant-ph · 2026-04-10 · unverdicted · novelty 1.0

A review of quantum uncertainty relations that covers foundational and practical applications of both variance- and entropy-based uncertainties.

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Showing 2 of 2 citing papers.

  • Security of deterministic key distribution with higher-dimensional systems quant-ph · 2025-05-22 · unverdicted · none · ref 39 · internal anchor

    Higher-dimensional two-way QKD protocols using mutually unbiased bases and Heisenberg-Weyl operators yield secret keys for stronger individual attacks and improved robustness to collective eavesdropping via entropic uncertainty relations.

  • Quantum Uncertainty and Entropy quant-ph · 2026-04-10 · unverdicted · none · ref 140 · internal anchor

    A review of quantum uncertainty relations that covers foundational and practical applications of both variance- and entropy-based uncertainties.