Utilization of Renewable energy for Industrial Applications using Quantum Computing

Rajawat, Anand Singh; Pant, Piyush; Goyal, S. B.

doi:10.58260/j.nras.2202.0102

Cited by 2 publications

(2 citation statements)

References 14 publications

(14 reference statements)

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“…Before feeding your data into your QDNN, you'll need to prepare it by splitting it up into a training set and a testing set. To fine-tune your QDNN's settings [19],also need to develop a learning algorithm. Learning algorithms can take many forms, and examples include quantum annealing and gradient descent.…”

Section: Gas Limit Attacksmentioning

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

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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.

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Section: Gas Limit Attacksmentioning

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

“…The present issues with renewable energy might be made worse by quantum computing. The challenges that present technology may face in the future may be solved by quantum computing [ 207 ]. Quantum computers are highly successful in handling the intricate problems associated with optimizing renewable energy sources.…”

Section: Implications and Potential Impactmentioning

confidence: 99%