Quantum key distribution as a quantum machine learning task
Abstract We propose considering Quantum Key Distribution (QKD) protocols as a use case for Quantum Machine Learning (QML) algorithms. We define and investigate the QML task of optimizing eavesdropping attacks on the quantum circuit implementation of the BB84 protocol. QKD protocols are well understo...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
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| Series: | npj Quantum Information |
| Online Access: | https://doi.org/10.1038/s41534-025-01088-9 |
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| _version_ | 1849234460800712704 |
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| author | Thomas Decker Marcelin Gallezot Sven Florian Kerstan Alessio Paesano Anke Ginter Wadim Wormsbecher |
| author_facet | Thomas Decker Marcelin Gallezot Sven Florian Kerstan Alessio Paesano Anke Ginter Wadim Wormsbecher |
| author_sort | Thomas Decker |
| collection | DOAJ |
| description | Abstract We propose considering Quantum Key Distribution (QKD) protocols as a use case for Quantum Machine Learning (QML) algorithms. We define and investigate the QML task of optimizing eavesdropping attacks on the quantum circuit implementation of the BB84 protocol. QKD protocols are well understood and solid security proofs exist enabling an easy evaluation of the QML model performance. The power of easy-to-implement QML techniques is shown by finding the explicit circuit for optimal individual attacks in a noise-free setting. For the noisy setting we find, to the best of our knowledge, a new cloning algorithm, which can outperform known cloning methods. Finally, we present a QML construction of a collective attack by using classical information from QKD post-processing within the QML algorithm. |
| format | Article |
| id | doaj-art-930b04ddf7d4454db20b7b7b7fb9299c |
| institution | Kabale University |
| issn | 2056-6387 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Quantum Information |
| spelling | doaj-art-930b04ddf7d4454db20b7b7b7fb9299c2025-08-20T04:03:07ZengNature Portfolionpj Quantum Information2056-63872025-08-011111610.1038/s41534-025-01088-9Quantum key distribution as a quantum machine learning taskThomas Decker0Marcelin Gallezot1Sven Florian Kerstan2Alessio Paesano3Anke Ginter4Wadim Wormsbecher5JoS QUANTUM GmbHJoS QUANTUM GmbHJoS QUANTUM GmbHJoS QUANTUM GmbHBundesdruckerei GmbHBundesdruckerei GmbHAbstract We propose considering Quantum Key Distribution (QKD) protocols as a use case for Quantum Machine Learning (QML) algorithms. We define and investigate the QML task of optimizing eavesdropping attacks on the quantum circuit implementation of the BB84 protocol. QKD protocols are well understood and solid security proofs exist enabling an easy evaluation of the QML model performance. The power of easy-to-implement QML techniques is shown by finding the explicit circuit for optimal individual attacks in a noise-free setting. For the noisy setting we find, to the best of our knowledge, a new cloning algorithm, which can outperform known cloning methods. Finally, we present a QML construction of a collective attack by using classical information from QKD post-processing within the QML algorithm.https://doi.org/10.1038/s41534-025-01088-9 |
| spellingShingle | Thomas Decker Marcelin Gallezot Sven Florian Kerstan Alessio Paesano Anke Ginter Wadim Wormsbecher Quantum key distribution as a quantum machine learning task npj Quantum Information |
| title | Quantum key distribution as a quantum machine learning task |
| title_full | Quantum key distribution as a quantum machine learning task |
| title_fullStr | Quantum key distribution as a quantum machine learning task |
| title_full_unstemmed | Quantum key distribution as a quantum machine learning task |
| title_short | Quantum key distribution as a quantum machine learning task |
| title_sort | quantum key distribution as a quantum machine learning task |
| url | https://doi.org/10.1038/s41534-025-01088-9 |
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