Cost-Optimization-Based Quantum Key Distribution over Quantum Key Pool Optical Networks

Jia, Jie; Dong, Bi‐Zhen; Kang, Le; Xie, Huanwen; Guo, Banghong

doi:10.3390/e25040661

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…Quantum keys are a very valuable resource and to manage and store them, a QKP is usually configured between each pair of neighboring nodes. i.e., a QKP is a repository containing multiple pre-generated quantum keys [34,35]. The sender and receiver use these keys to encrypt and decrypt the communication data during the QKD process.…”

Section: Design Of Qkpmentioning

confidence: 99%

A Dynamic-Routing Algorithm Based on a Virtual Quantum Key Distribution Network

Miao

2023

Applied Sciences

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Quantum key distribution (QKD) is an encrypted communication technique based on the principles of quantum mechanics that ensures communication security by exploiting the properties of quantum states. Currently, the transmission efficiency of the QKD system is low. Trusted relay technology is used to solve this problem and achieve long-distance transmission. However, trusted relaying alone cannot decrypt the issues of poor link stability and the low utilization of key resources. To further optimize the system performance, we propose a dynamic routing algorithm. One of the improvement schemes includes the following: firstly, an adjustable-size quantum key pool (QKP) is designed, which can dynamically adjust the size of the refreshing pool according to the actual demand. Secondly, the utilization of key resources is improved by using the residual quantum key model to dynamically obtain the remaining key amount in the QKP and set the key amount threshold. We calculate the link-blocking probability and track the blocking intensity and blocking entry by combining the Poisson process, thus realizing the evaluation of the link stability. Finally, the number of remaining keys in the QKP and the link-blocking probability combine with the random wandering model as the basis of the route selection for the QKD dynamic routing algorithm to achieve efficient key path selection. We validated the algorithm by comparing it with other algorithms on the Mininet simulation platform, and the algorithm proved to have a better performance in terms of congestion avoidance, delay reduction, and improved QKD efficiency. This scheme provides a novel and efficient way to solve the problems in existing QKD systems. It effectively improves the transmission efficiency and strengthens the system’s security by dynamically obtaining the critical volume, accurately evaluating the link state, and selecting the optimal critical path.

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Section: Design Of Qkpmentioning

confidence: 99%

A Dynamic-Routing Algorithm Based on a Virtual Quantum Key Distribution Network

Miao

2023

Applied Sciences

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“…The former part represents the construction and transportation cost, and the latter part demonstrates the transportation costs of both empty and fully loaded PBs. The weight capacity of the truck is given in Equation (24).…”

Section: Define Objective Functionmentioning

confidence: 99%

“…In [23], the researchers promote the latest vehicle classification and detection system for smart traffic monitoring with the support of a Convolutional Neural Network (CNN) to fragment aerial imagery. Another study presents an enhanced strategy for a Quantum Key Distribution (QKD) network structure and cost analysis [24]. In particular, a new Hybridized-QKD-Network-Cost (HQNC) heuristic algorithm is implemented to resolve the cost optimization issue.…”

Section: Introductionmentioning

confidence: 99%

Modified Gannet Optimization Algorithm for Reducing System Operation Cost in Engine Parts Industry with Pooling Management and Transport Optimization

Alkahtani,

Abidi,

Obaid

et al. 2023

Sustainability

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Due to the emergence of technology, electric motors (EMs), an essential part of electric vehicles (which basically act as engines), have become a pivotal component in modern industries. Monitoring the spare parts of EMs is critical for stabilizing and managing industrial parts. Generally, the engine or motor parts are delivered to factories using packing boxes (PBs). This is mainly achieved via a pooling center that manages the operation and transportation costs. Nevertheless, this process has some drawbacks, such as a high power train, bad press, and greater energy and time consumption, resulting in performance degradation. Suppliers generally take the parts from one place and deliver them to the other, which leads to more operation and transportation costs. Instead, it requires pooling centers to act as hubs, at which every supplier collects the material. This can mitigate the cost level. Moreover, choosing the placement of pooling centers is quite a challenging task. Different methods have been implemented; however, optimal results are still required to achieve better objectives. This paper introduces a novel concept for pooling management and transport optimization of engine parts to overcome the issues in traditional solution methodologies. The primary intention of this model is to deduce the total cost of the system operation and construction. Programming techniques for transporting the PBs, as well as for locating the pooling center, are determined with the aid of an objective function as a cost function. The location of the pooling center’s cost is optimized, and a Modified Gannet Optimization Algorithm (MGOA) is proposed. Using this method, the proposed model is validated over various matrices, and the results demonstrate its better efficiency rate.

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Advances in artificial intelligence and machine learning for quantum communication applications

Mafu

2024

IET Quantum Communication

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Artificial intelligence (AI) and classical machine learning (ML) techniques have revolutionised numerous fields, including quantum communication. Quantum communication technologies rely heavily on quantum resources, which can be challenging to produce, control, and maintain effectively to ensure optimum performance. ML has recently been applied to quantum communication and networks to mitigate noise‐induced errors and analyse quantum protocols. The authors systematically review state‐of‐the‐art ML applications to advance theoretical and experimental central quantum communication protocols, specifically quantum key distribution, quantum teleportation, quantum secret sharing, and quantum networks. Specifically, the authors survey the progress on how ML and, more broadly, AI techniques have been applied to optimise various components of a quantum communication system. This has resulted in ultra‐secure quantum communication protocols with optimised key generation rates as well as efficient and robust quantum networks. Integrating AI and ML techniques opens intriguing prospects for securing and facilitating efficient and reliable large‐scale communication between multiple parties. Most significantly, large‐scale communication networks have the potential to gradually develop the maturity of a future quantum internet.

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Cost-Optimization-Based Quantum Key Distribution over Quantum Key Pool Optical Networks

Cited by 4 publications

References 32 publications

A Dynamic-Routing Algorithm Based on a Virtual Quantum Key Distribution Network

A Dynamic-Routing Algorithm Based on a Virtual Quantum Key Distribution Network

Modified Gannet Optimization Algorithm for Reducing System Operation Cost in Engine Parts Industry with Pooling Management and Transport Optimization

Advances in artificial intelligence and machine learning for quantum communication applications

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