arXiv — Machine Learning · · 3 min read

Deployed trusted-node quantum key distribution over 300 km with a multi-core fiber access link

Mirrored from arXiv — Machine Learning for archival readability. Support the source by reading on the original site.

Quantum Physics

arXiv:2606.06107 (quant-ph)
[Submitted on 4 Jun 2026]

Title:Deployed trusted-node quantum key distribution over 300 km with a multi-core fiber access link

View a PDF of the paper titled Deployed trusted-node quantum key distribution over 300 km with a multi-core fiber access link, by Martin Clason and 10 other authors
View PDF HTML (experimental)
Abstract:Quantum key distribution (QKD) is increasingly considered for deployment in realistic communication networks, where long distances, heterogeneous fiber infrastructure, and coexistence with classical traffic present substantial challenges. Here, we demonstrate trusted-node QKD between Linköping University and the Stockholm hub of the Swedish national quantum communication infrastructure over 270 km of deployed single-mode fiber, extended by a 33 km multi-core fiber (MCF) segment emulating a metropolitan access link, for a total distance of 303 km. The two sub-links use commercial QKD systems whose receivers are interfaced with external superconducting nanowire single-photon detectors, enabling operation at losses beyond those supported by standard internal gated-mode detectors. We operate the link while actively switching the QKD channel between two MCF cores, with co-propagating Ethernet traffic and injected broadband optical noise in the other cores. The results demonstrate the integration of commercial QKD into demanding, dynamically reconfigurable fiber infrastructure relevant to future hybrid quantum-classical networks. Finally, using the generated secret keys, we illustrate how limited and time-varying QKD throughput affects one-time-pad-protected image transmission: image fidelity depends strongly on the available QKD-generated key budget and the choice of compression algorithm, highlighting application-level challenges for QKD-based encryption in realistic scenarios.
Comments: 11 pages, 4 figures
Subjects: Quantum Physics (quant-ph); Information Theory (cs.IT); Machine Learning (cs.LG); Image and Video Processing (eess.IV); Optics (physics.optics)
Cite as: arXiv:2606.06107 [quant-ph]
  (or arXiv:2606.06107v1 [quant-ph] for this version)
  https://doi.org/10.48550/arXiv.2606.06107
arXiv-issued DOI via DataCite

Submission history

From: Martin Clason [view email]
[v1] Thu, 4 Jun 2026 12:53:46 UTC (637 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled Deployed trusted-node quantum key distribution over 300 km with a multi-core fiber access link, by Martin Clason and 10 other authors
  • View PDF
  • HTML (experimental)
  • TeX Source

Current browse context:

quant-ph
< prev   |   next >
Loading...

BibTeX formatted citation

loading...
Data provided by:

Bookmark

BibSonomy Reddit
Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos

Demos

Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers

Recommenders and Search Tools

Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Discussion (0)

Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.

Sign in →

No comments yet. Sign in and be the first to say something.

More from arXiv — Machine Learning