DQRA: Deep Quantum Routing Agent for Entanglement Routing in Quantum Networks

Le, Linh; Nguyen, Tu N.

doi:10.1109/tqe.2022.3148667

Cited by 13 publications

(3 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is one of the real-world applications of quantum-enhanced machine learning. Quantum networking and the Applications That Could Be Developed Using It Le and Nguyen [9] advocated the use of DQRA as a protocol that is tailored to the requirements of entanglement routing in quantum networks. Their work contributes to the greater goal of bringing quantum communication closer to the point where it can be used in practical settings.…”

Section: Related Workmentioning

confidence: 99%

Quantum Deep Neural Network Based Classification of Attack Vectors on the Ethereum Blockchain

Rajawat,

Goyal,

Kumar

et al. 2024

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

INTRODUCTION: The implementation of robust security protocols is imperative in light of the exponential growth of blockchain-based platforms such as Ethereum. The importance of developing more effective strategies to detect and counter potential attacks is growing in tandem with the sophistication of the methods employed by attackers. In this study, we present a novel approach that leverages quantum computing to identify and predict attack vectors on the Ethereum blockchain. OBJECTIVES: The primary objective of this study is to suggest an innovative methodology for enhancing the security of Ethereum by leveraging quantum computing. The purpose of this study is to demonstrate that QRBM and QDN are efficient in identifying and predicting security flaws in blockchain transactions. METHODS: We combined methods from quantum computing with social network research approaches. An enormous dataset containing both genuine Ethereum transactions and a carefully chosen spectrum of malicious activity indicative of popular attack vectors was used to train our model, the QRBM. Thanks to the dataset, the QRBM was able to learn to distinguish between typical and out-of-the-ordinary activities. RESULTS: In comparison to more conventional deep learning models, the QRBM showed substantially better accuracy when it came to identifying transaction behaviours. The model's improved scalability and efficiency were made possible by its quantum nature, which is defined by features like entanglement and superposition. Specifically, the QRBM handled non-informative inputs better and solved problems faster. CONCLUSION: This study paves the way for further investigation into quantum-enhanced cybersecurity measures and highlights the promise of quantum neural networks in strengthening the security of blockchain technology. According to our research, quantum computing has the potential to be an essential tool in creating Ethereum-style blockchain security systems that are more advanced, efficient, and resilient.

show abstract

Section: Related Workmentioning

confidence: 99%

Quantum Deep Neural Network Based Classification of Attack Vectors on the Ethereum Blockchain

Rajawat,

Goyal,

Kumar

et al. 2024

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

show abstract

“…Subsequently, a quantum-inspired genetic algorithm is proposed called quantum approximate optimization algorithm (QAOA) for effective service execution based on policy gradient learning strategy to resolve the device-centric issues based on hypothesis data collected and processed asynchronously to improve the system performance accuracy [16] and is treated as A1. In [17], the researchers design a machine learning architecture-based novel quantum service allocation model to address the next-generation network gaps. This algorithm called the qubit-preserved shortest path algorithm (DQRA), assesses the reward function to increase the service execution rate in each routing window.…”

Section: A Accurate Data Fusion Importance Based On Quantum Theorymentioning

confidence: 99%

A Quantum-Inspired Sensor Consolidation Measurement Approach for Cyber-Physical Systems

Mekala,

Srivastava,

Gandomi

et al. 2024

IEEE Trans. Netw. Sci. Eng.

View full text Add to dashboard Cite

Cyber-Physical System (CPS) devices interconnect to grab data over a common platform from industrial applications. Maintaining immense data and making instant decision analysis by selecting a feasible node to meet latency constraints is challenging. To address this issue, we design a quantuminspired online node consolidation (QONC) algorithm based on a time-sensitive measurement reinforcement system for making decisions to evaluate the feasible node, ensuring reliable service and deploying the node at the appropriate position for accurate data computation and communication. We design the Angularbased node position analysis method to localize the node through rotation and t-gate to mitigate latency and enhance system performance. We formalize the estimation and selection of the feasible node based on quantum formalization node parameters (node contiguity, node optimal knack rate, node heterogeneity, probability of fusion variance error ratio). We design a fitness function to assess the probability of node fitness before selection. The simulation results convince us that our approach achieves an effective measurement rate of performance index by reducing the average error ratio from 0.17-0.22, increasing the average coverage ratio from 29% to 42%, and the qualitative execution frequency of services. Moreover, the proposed model achieves a 74.3% offloading reduction accuracy and a 70.2% service reliability rate compared to state-of-the-art approaches. Our system is scalable and efficient under numerous simulation frameworks.

show abstract

“…Therefore, the entanglement routing with active wavelength multiplexing schemes is desiderated for constructing entanglement distribution networks with various topologies [ 42 , 43 , 44 , 45 ]. In the meantime, existing quantum routing schemes [ 2 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ] are mainly focused on trusted node networks and quantum memory-assisted networks, and cannot be applied to quantum entanglement distribution networks, where time division multiplexing (TDM) and wavelength division multiplexing (WDM) techniques need to be adopted to forward entangled photons.…”

Section: Introductionmentioning

confidence: 99%

First Request First Service Entanglement Routing Scheme for Quantum Networks

Tang

Han

et al. 2022

Entropy

View full text Add to dashboard Cite

Quantum networks enable many applications beyond the reach of classical networks by supporting the establishment of long-distance entanglement connections, and are already stepped into the entanglement distribution network stage. The entanglement routing with active wavelength multiplexing schemes is urgently required for satisfying the dynamic connection demands of paired users in large-scale quantum networks. In this article, the entanglement distribution network is modeled into a directed graph, where the internal connection loss among all ports within a node is considered for each supported wavelength channel, which is quite different to classical network graphs. Afterwards, we propose a novel first request first service (FRFS) entanglement routing scheme, which performs the modified Dijkstra algorithm to find out the lowest loss path from the entangled photon source to each paired user in order. Evaluation results show that the proposed FRFS entanglement routing scheme can be applied to large-scale and dynamic topology quantum networks.

show abstract

DQRA: Deep Quantum Routing Agent for Entanglement Routing in Quantum Networks

Abstract: IEEE Transactions on Quantum EngineeringDate of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 13 publications

References 36 publications

Quantum Deep Neural Network Based Classification of Attack Vectors on the Ethereum Blockchain

Quantum Deep Neural Network Based Classification of Attack Vectors on the Ethereum Blockchain

A Quantum-Inspired Sensor Consolidation Measurement Approach for Cyber-Physical Systems

First Request First Service Entanglement Routing Scheme for Quantum Networks

Contact Info

Product

Resources

About