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Ahmed Farouk

Publications and source records attributed to Ahmed Farouk.

33 records · Page 2Linked to original sources
arXiv2025

Optimizing Low-Energy Carbon IIoT Systems with Quantum Algorithms: Performance Evaluation and Noise Robustness

Low-energy carbon Internet of Things (IoT) systems are essential for sustainable development, as they reduce carbon emissions while ensuring efficient device performance. Although classical algorithms manage energy efficiency and data processing within these systems, they often face scalability and real-time processing limitations. Quantum algorithms offer a solution to these challenges by delivering faster computations and improved optimization, thereby enhancing both the performance and sustainability of low-energy carbon IoT systems. Therefore, we introduced three quantum algorithms: quantum neural networks utilizing Pennylane (QNN-P), Qiskit (QNN-Q), and hybrid quantum neural networks (QNN-H). These algorithms are applied to two low-energy carbon IoT datasets room occupancy detection (RODD) and GPS tracker (GPSD). For the RODD dataset, QNN-P achieved the highest accuracy at 0.95, followed by QNN-H at 0.91 and QNN-Q at 0.80. Similarly, for the GPSD dataset, QNN-P attained an accuracy of 0.94, QNN-H 0.87, and QNN-Q 0.74. Furthermore, the robustness of these models is verified against six noise models. The proposed quantum algorithms demonstrate superior computational efficiency and scalability in noisy environments, making them highly suitable for future low-energy carbon IoT systems. These advancements pave the way for more sustainable and efficient IoT infrastructures, significantly minimizing energy consumption while maintaining optimal device performance.

quant-ph↗
arXiv2025

QDCNN: Quantum Deep Learning for Enhancing Safety and Reliability in Autonomous Transportation Systems

In transportation cyber-physical systems (CPS), ensuring safety and reliability in real-time decision-making is essential for successfully deploying autonomous vehicles and intelligent transportation networks. However, these systems face significant challenges, such as computational complexity and the ability to handle ambiguous inputs like shadows in complex environments. This paper introduces a Quantum Deep Convolutional Neural Network (QDCNN) designed to enhance the safety and reliability of CPS in transportation by leveraging quantum algorithms. At the core of QDCNN is the UU† method, which is utilized to improve shadow detection through a propagation algorithm that trains the centroid value with preprocessing and postprocessing operations to classify shadow regions in images accurately. The proposed QDCNN is evaluated on three datasets on normal conditions and one road affected by rain to test its robustness. It outperforms existing methods in terms of computational efficiency, achieving a shadow detection time of just 0.0049352 seconds, faster than classical algorithms like intensity-based thresholding (0.03 seconds), chromaticity-based shadow detection (1.47 seconds), and local binary pattern techniques (2.05 seconds). This remarkable speed, superior accuracy, and noise resilience demonstrate the key factors for safe navigation in autonomous transportation in real-time. This research demonstrates the potential of quantum-enhanced models in addressing critical limitations of classical methods, contributing to more dependable and robust autonomous transportation systems within the CPS framework.

quant-ph↗
arXiv2025

QNN-VRCS: A Quantum Neural Network for Vehicle Road Cooperation Systems

The escalating complexity of urban transportation systems, exacerbated by factors such as traffic congestion, diverse transportation modalities, and shifting commuter preferences, necessitates the development of more sophisticated analytical frameworks. Traditional computational approaches often struggle with the voluminous datasets generated by real-time sensor networks, and they generally lack the precision needed for accurate traffic prediction and efficient system optimization. This research integrates quantum computing techniques to enhance Vehicle Road Cooperation Systems (VRCS). By leveraging quantum algorithms, specifically $UU^{\dagger}$ and variational $UU^{\dagger}$, in conjunction with quantum image encoding methods such as Flexible Representation of Quantum Images (FRQI) and Novel Enhanced Quantum Representation (NEQR), we propose an optimized Quantum Neural Network (QNN). This QNN features adjustments in its entangled layer structure and training duration to better handle the complexities of traffic data processing. Empirical evaluations on two traffic datasets show that our model achieves superior classification accuracies of 97.42% and 84.08% and demonstrates remarkable robustness in various noise conditions. This study underscores the potential of quantum-enhanced 6G solutions in streamlining complex transportation systems, highlighting the pivotal role of quantum technologies in advancing intelligent transportation solutions.

quant-ph↗
arXiv2025

SentiQNF: A Novel Approach to Sentiment Analysis Using Quantum Algorithms and Neuro-Fuzzy Systems

Sentiment analysis is an essential component of natural language processing, used to analyze sentiments, attitudes, and emotional tones in various contexts. It provides valuable insights into public opinion, customer feedback, and user experiences. Researchers have developed various classical machine learning and neuro-fuzzy approaches to address the exponential growth of data and the complexity of language structures in sentiment analysis. However, these approaches often fail to determine the optimal number of clusters, interpret results accurately, handle noise or outliers efficiently, and scale effectively to high-dimensional data. Additionally, they are frequently insensitive to input variations. In this paper, we propose a novel hybrid approach for sentiment analysis called the Quantum Fuzzy Neural Network (QFNN), which leverages quantum properties and incorporates a fuzzy layer to overcome the limitations of classical sentiment analysis algorithms. In this study, we test the proposed approach on two Twitter datasets: the Coronavirus Tweets Dataset (CVTD) and the General Sentimental Tweets Dataset (GSTD), and compare it with classical and hybrid algorithms. The results demonstrate that QFNN outperforms all classical, quantum, and hybrid algorithms, achieving 100% and 90% accuracy in the case of CVTD and GSTD, respectively. Furthermore, QFNN demonstrates its robustness against six different noise models, providing the potential to tackle the computational complexity associated with sentiment analysis on a large scale in a noisy environment. The proposed approach expedites sentiment data processing and precisely analyses different forms of textual data, thereby enhancing sentiment classification and insights associated with sentiment analysis.

cs.CL↗
arXiv2024

Towards Scalable Quantum Key Distribution: A Machine Learning-Based Cascade Protocol Approach

Quantum Key Distribution (QKD) is a pivotal technology in the quest for secure communication, harnessing the power of quantum mechanics to ensure robust data protection. However, scaling QKD to meet the demands of high-speed, real-world applications remains a significant challenge. Traditional key rate determination methods, dependent on complex mathematical models, often fall short in efficiency and scalability. In this paper, we propose an approach that involves integrating machine learning (ML) techniques with the Cascade error correction protocol to enhance the scalability and efficiency of QKD systems. Our ML-based approach utilizes an autoencoder framework to predict the Quantum Bit Error Rate (QBER) and final key length with over 99\% accuracy. This method significantly reduces error correction time, maintaining a consistently low computation time even with large input sizes, such as data rates up to 156 Mbps. In contrast, traditional methods exhibit exponentially increasing computation times as input sizes grow, highlighting the superior scalability of our ML-based solution. Through comprehensive simulations, we demonstrate that our method not only accelerates the error correction process but also optimizes resource utilization, making it more cost-effective and practical for real-world deployment. The Cascade protocol's integration further enhances system security by dynamically adjusting error correction based on real-time QBER observations, providing robust protection against potential eavesdropping. Our research establishes a new benchmark for scalable, high-throughput QKD systems, proving that machine learning can significantly advance the field of quantum cryptography. This work continues the evolution towards truly scalable quantum communication.

quant-ph↗
arXiv2024

NEQRX: Efficient Quantum Image Encryption with Reduced Circuit Complexity

Cryptography plays an important role in ensuring data security and authentication within information processing systems. As the prevalence of digital imagery continues to grow, safeguarding this form of data becomes increasingly crucial. However, existing security protocols, reliant on complex mathematical models, exhibit vulnerabilities in effectively protecting information from both internal and external threats. Moreover, the forthcoming advent of quantum computing poses a significant challenge, as it could decrypt data encrypted by classical. In this paper, we propose an efficient implementation scheme for a quantum image encryption algorithm combining the generalized affine transform and logistic map. We evaluated developed quantum circuits using qiskit and quantum devices to validate the encryption technique. Through comprehensive performance analysis, we have demonstrated the efficiency of the chosen encryption algorithm across various criteria. Furthermore, we introduce a hybrid methodology aimed at mitigating circuit complexity and reducing quantum cost. Leveraging the Espresso algorithm and incorporating an ancilla qubit into the circuitry, we achieve a remarkable 50\% reduction in cost while maintaining security and efficiency. Finally, we conducted robustness and security analyses to assess the resilience of our encryption method against diverse noise attacks. The results confirm that our proposed quantum image encryption technique provides a secure solution and offers precise and measurable quantum image processing capabilities.

quant-ph↗
arXiv2023

5G/6G-Enabled Metaverse Technologies: Taxonomy, Applications, and Open Security Challenges with Future Research Directions

Internet technology has proven to be a vital contributor to many cutting-edge innovations that have given humans access to interact virtually with objects. Until now, numerous virtual systems had been developed for digital transformation to enable access to thousands of services and applications that range from virtual gaming to social networks. However, the majority of these systems lack to maintain consistency during interconnectivity and communication. To explore this discussion, in the recent past a new term, Metaverse has been introduced, which is the combination of meta and universe that describes a shared virtual environment, where a number of technologies, such as 4th and 5th generation technologies, VR, ML algorithms etc., work collectively to support each other for the sake of one objective, which is the virtual accessibility of objects via one network platform. With the development, integration, and virtualization of technologies, a lot of improvement in daily life applications is expected, but at the same time, there is a big challenge for the research community to secure this platform from external and external threats, because this technology is exposed to many cybersecurity attacks. Hence, it is imperative to systematically review and understand the taxonomy, applications, open security challenges, and future research directions of the emerging Metaverse technologies. In this paper, we have made useful efforts to present a comprehensive survey regarding Metaverse technology by taking into account the aforesaid parameters. Following this, in the initial phase, we explored the future of Metaverse in the presence of 4th and 5th generation technologies. Thereafter, we discussed the possible attacks to set a preface for the open security challenges. Based on that, we suggested potential research directions that could be beneficial to address these challenges cost-effectively.

cs.CR↗
arXiv2022

Solving Vehicle Routing Problem Using Quantum Approximate Optimization Algorithm

In this paper, we describe the usage of the Quantum Approximate Optimization Algorithm (QAOA), which is a quantum-classical heuristic, to solve a combinatorial optimization and integer programming task known as Vehicle Routing Problem (VRP). We outline the Ising formulation for VRP and present a detailed procedure to solve VRP by minimizing its simulated Ising Hamiltonian using the IBM Qiskit platform. Here, we attempt to find solutions for the VRP problems: (4,2), (5,2), and (5,3), where each (n, k) represents a VRP problem with n locations and k vehicles. We find that the performance of QAOA is not just dependent upon the classical optimizer used, the number of steps p in which an adiabatic path is realized, or the way parameters are initialized, but also on the problem instance itself.

quant-ph↗
arXiv2022

Efficient quantum non-fungible tokens for blockchain

Blockchain is a decentralized system that allows transaction transmission and storage according to the roles of the Consensus algorithm and Smart contracts. Non-fungible tokens (NFTs) consolidate the best characteristics of blockchain technology to deliver unique and bona fide tokens, each with distinctive attributes with non-fungible resources. Unfortunately, current classical NFTs are suffering from high costs regarding the consumed power of mining and lack of security. Therefore, this paper presents a new protocol for preparing quantum non-fungible tokens where a quantum state representing NFT is mounted on a blockchain instead of physically giving it to the owner. The proposed scheme is simulated and analyzed against various attacks and proves its ability to secure against them. Furthermore, the presented protocol provides reliable and cheaper NFTs than the classical one.

cs.CR↗
arXiv2021

Hash-MAC-DSDV: Mutual Authentication for Intelligent IoT-Based Cyber-Physical Systems

Cyber-Physical Systems (CPS) connected in the form of Internet of Things (IoT) are vulnerable to various security threats, due to the infrastructure-less deployment of IoT devices. Device-to-Device (D2D) authentication of these networks ensures the integrity, authenticity, and confidentiality of information in the deployed area. The literature suggests different approaches to address security issues in CPS technologies. However, they are mostly based on centralized techniques or specific system deployments with higher cost of computation and communication. It is therefore necessary to develop an effective scheme that can resolve the security problems in CPS technologies of IoT devices. In this paper, a lightweight Hash-MAC-DSDV (Hash Media Access Control Destination Sequence Distance Vector) routing scheme is proposed to resolve authentication issues in CPS technologies, connected in the form of IoT networks. For this purpose, a CPS of IoT devices (multi-WSNs) is developed from the local-chain and public chain, respectively. The proposed scheme ensures D2D authentication by the Hash-MAC-DSDV mutual scheme, where the MAC addresses of individual devices are registered in the first phase and advertised in the network in the second phase. The proposed scheme allows legitimate devices to modify their routing table and unicast the one-way hash authentication mechanism to transfer their captured data from source towards the destination. Our evaluation results demonstrate that Hash- MAC-DSDV outweighs the existing schemes in terms of attack detection, energy consumption and communication metrics.

cs.CR↗
arXiv2019

Entanglement control of two-level atoms in dissipative cavities

An open quantum bipartite system consisting of two independent two-level atoms interacting non-linearly with a two-mode electromagnetic cavity field is investigated by proposing a suitable non-Hermitian generalization of Hamiltonian. The mathematical procedure of obtaining the corresponding wave function of the system is clearly given. Panchartnam phase is studied to give a precise information about the required initial system state, which is related to artificial phase jumps, to control the Degree of Entanglement (DEM) and get the highest Concurrence. We discuss the effect of time-variation coupling, and dissipation of both atoms and cavity. The effect of the time-variation function appears as frequency modulation (FM) effect in the radio waves. Concurrence rapidly reaches the disentangled state (death of entanglement) by increasing the effect of field decay. On the contrary, the atomic decay has no effect.

quant-ph↗
arXiv2018

A General protocol for Distributed Quantum Gates

Distributed quantum computation requires to apply quantum remote gates on separate nodes or subsystems of network. On the other hand, Toffoli gate is a universal and well-known quantum gate. It is frequently used in synthesis of quantum circuits. In this paper, a general protocol for implementing a remote n-qubit controlled-U gate is presented with minimum required resources. Then, the proposed method is applied for implementing a Toffoli gate in bipartite and tripartite systems. This method also is optimal when group of the qubits belong to one part, section or subsystem of network. Because it only use one entangled qubit for each group of the qubits in mentioned conditions.

quant-ph↗
arXiv2018

Bidirectional Quantum Controlled Teleportation by Using Five-qubit Entangled State as a Quantum Channel

In this paper, a novel protocol is proposed for implementing BQCT by using five-qubit en The proposed protocol depends on the Controlled-NOT operation, proper single-qubit unitary operations and single-qubit measurement in the Z-basis and X-basis. The results showed that the protocol is more efficient from the perspective such as lower shared qubits and, single qubit measurements compared to the previous work. Furthermore, the probability of obtaining Charlie's qubit by eavesdropper is reduced, and supervisor can control one of the users or every two users. Also, we present a new method for transmitting n and m-qubits entangled states between Alice and Bob using proposed protocol.tangled states as a quantum channel which in the same time, the communicated users can teleport each one-qubit state to each other under permission of controller.

quant-ph↗
arXiv2016

A generalized architecture of quantum secure direct communication for N disjointed users with authentication

In this paper, we generalize a secured direct communication process between N users with partial and full cooperation of quantum server. The security analysis of authentication and communication processes against many types of attacks proved that the attacker cannot gain any information during intercepting either authentication or communication processes. Hence, the security of transmitted message among N users is ensured as the attacker introduces an error probability irrespective of the sequence of measurement.

cs.CR↗