Field-Trial of Machine Learning-Assisted Quantum Key Distribution (QKD) Networking with SDN

Ou, Yanni; Hugues-Salas, E.; Ntavou, Foteini; Bi, Yu; Yan, Sy.; Kanellos, George T.; Nejabati, Reza; Simeonidou, Dimitra

doi:10.1109/ecoc.2018.8535497

Cited by 25 publications

(20 citation statements)

References 3 publications

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“…Other examples of how the SDN and network function virtualization (NFV) paradigms-widely adopted in classical networks-can be extended to quantum networks, including internet of Things (IoT) [54] and 5G [55] applications, are reported in [55][56][57][58][59][60][61][62][63], further proving the importance of moving from current point-to-point setups to more complex topologies. Furthermore, recent advances in machine learning (ML) can greatly help to improve the automation of QKD systems, whose performance depends of many parameters that must be finely tuned, thereby significantly decreasing operational and maintenance costs.…”

Section: Qkd Networkingmentioning

confidence: 99%

Secure Quantum Communication Technologies and Systems: From Labs to Markets

et al. 2020

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We provide a broad overview of current quantum communication by analyzing the recent discoveries on the topic and by identifying the potential bottlenecks requiring further investigation. The analysis follows an industrial perspective, first identifying the state or the art in terms of protocols, systems, and devices for quantum communication. Next, we classify the applicative fields where short- and medium-term impact is expected by emphasizing the potential and challenges of different approaches. The direction and the methodology with which the scientific community is proceeding are discussed. Finally, with reference to the European guidelines within the Quantum Flagship initiative, we suggest a roadmap to match the effort community-wise, with the objective of maximizing the impact that quantum communication may have on our society.

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Section: Qkd Networkingmentioning

confidence: 99%

Secure Quantum Communication Technologies and Systems: From Labs to Markets

et al. 2020

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“…In recent years, the desire to reduce the capital expenditures of QKD network deployment has motivated the research of QKD integration with classical networks, where both of physical-layer performance and network-layer performance are taken into account. In order to improve the physical-layer performance such as secret-key rate and achievable distance, a number of analytical studies [30], [43], system experiments [31], [34], [36], and field trials [33], [44] have been carried out. On the other hand, several resource assignment strategies have been proposed to optimize the network-layer performance such as blocking probability and resource utilization when QKD coexists with the classical networks [32], [35].…”

Section: A Qkd Network Deploymentmentioning

confidence: 99%

“…In [50], the end-to-end key on demand service provisioning over a SDN-controlled QKD network was demonstrated. In [44], SDN was combined with machine learning to achieve dynamic and optimal wavelength allocation for both quantum and classical channels. Meanwhile, with regards to QKP technique, several QKPs were constructed to enable on-demand secret-key volume allocation for control channels and data channels in a software defined optical network [40].…”

Section: B Qkd Network Managementmentioning

confidence: 99%

Multi-Tenant Provisioning for Quantum Key Distribution Networks With Heuristics and Reinforcement Learning: A Comparative Study

Cao

Zhao

et al. 2020

IEEE Trans. Netw. Serv. Manage.

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Quantum key distribution (QKD) networks are potential to be widely deployed in the immediate future to provide long-term security for data communications. Given the high price and complexity, multi-tenancy has become a cost-effective pattern for QKD network operations. In this work, we concentrate on addressing the online multi-tenant provisioning (On-MTP) problem for QKD networks, where multiple tenant requests (TRs) arrive dynamically. On-MTP involves scheduling multiple TRs and assigning non-reusable secret keys derived from a QKD network to multiple TRs, where each TR can be regarded as a high-security-demand organization with the dedicated secret-key demand. The quantum key pools (QKPs) are constructed over QKD network infrastructure to improve management efficiency for secret keys. We model the secret-key resources for QKPs and the secret-key demands of TRs using distinct images. To realize efficient On-MTP, we perform a comparative study of heuristics and reinforcement learning (RL) based On-MTP solutions, where three heuristics (i.e., random, fit, and best-fit based On-MTP algorithms) are presented and a RL framework is introduced to realize automatic training of an On-MTP algorithm. The comparative results indicate that with sufficient training iterations the RL-based On-MTP algorithm significantly outperforms the presented heuristics in terms of tenant-request blocking probability and secret-key resource utilization.Index Terms-Quantum key distribution networks, online multi-tenant provisioning, heuristics, reinforcement learning.

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“…In 2017, Chistyakov et al used a subcarrier quantum system to propose a dynamic quantum routing and secure communication method based on OpenFlow protocol in SDN, further demonstrating the feasibility of applying SDN techniques to QKD networks [22]. In 2018, to effectively alleviate the problem of in-band noise in the QKD network, Ou et al used machine learning-based approach to estimate physical performances of quantum channel for the successful key generation and transmission [23]. In the same year, Hugues-Salas et al demonstrated that QKD resources can be successfully allocated through SDN control under DDoS attacks [24].…”

Section: Experimental Verification Of the Networkmentioning

confidence: 99%

Quantum-Key-Distribution (QKD) Networks Enabled by Software-Defined Networks (SDN)

2019

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As an important support for quantum communication, quantum key distribution (QKD) networks have achieved a relatively mature level of development, and they face higher requirements for multi-user end-to-end networking capabilities. Thus, QKD networks need an effective management plane to control and coordinate with the QKD resources. As a promising technology, software defined networking (SDN) can separate the control and management of QKD networks from the actual forwarding of the quantum keys. This paper systematically introduces QKD networks enabled by SDN, by elaborating on its overall architecture, related interfaces, and protocols. Then, three-use cases are provided as important paradigms with their corresponding schemes and simulation performances.

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Field-Trial of Machine Learning-Assisted Quantum Key Distribution (QKD) Networking with SDN

Abstract: We demonstrated, for the first time, a machine-learning method to assist the coexistence between quantum and classical communication channels. Software-defined networking was used to successfully enable the key generation and transmission over a city and campus network.

Cited by 25 publications

References 3 publications

Secure Quantum Communication Technologies and Systems: From Labs to Markets

Secure Quantum Communication Technologies and Systems: From Labs to Markets

Multi-Tenant Provisioning for Quantum Key Distribution Networks With Heuristics and Reinforcement Learning: A Comparative Study

Quantum-Key-Distribution (QKD) Networks Enabled by Software-Defined Networks (SDN)

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