The paper presents a novel Hybrid Quantum AI and Classical Machine Learning (CML) Model for Telecommunication Network Traffic Analysis, integrating quantum computing's computational advantages with classical machine learning's stability. The methodology employs Quantum Feature Embedding (QFE) using Amplitude Encoding (AE) and Angle Encoding (AnE) to transform classical data into quantum states, improving pattern recognition. Feature Engineering (FE) is performed using Pandas for data preprocessing, ensuring optimal input for hybrid processing. The hybrid model leverages Quantum Neural Networks (QNNs) and Quantum Kernel Methods (QKM) for high-dimensional feature extraction, while Convolutional Neural Networks (CNNs) and Random Forests (RF) enhance interpretability and stability. Performance benchmarking based on Accuracy (ACC), Execution Time (ET), and Computational Complexity (CC) demonstrates that the proposed model outperforms classical approaches in speed and efficiency. Simulations using IBM Qiskit and TensorFlow Quantum (TFQ) validate the effectiveness of this framework. The proposed approach provides a scalable and optimized solution for real-time telecommunication network traffic management, suggesting the technique for future development in quantum-enhanced AI applications.
The paper presents a novel Hybrid Quantum AI and Classical Machine Learning (CML) Model for Telecommunication Network Traffic Analysis, integrating quantum computing's computational advantages with classical machine learning's stability. The methodology employs Quantum Feature Embedding (QFE) using Amplitude Encoding (AE) and Angle Encoding (AnE) to transform classical data into quantum states, improving pattern recognition. Feature Engineering (FE) is performed using Pandas for data preprocessing, ensuring optimal input for hybrid processing. The hybrid model leverages Quantum Neural Networks (QNNs) and Quantum Kernel Methods (QKM) for high-dimensional feature extraction, while Convolutional Neural Networks (CNNs) and Random Forests (RF) enhance interpretability and stability. Performance benchmarking based on Accuracy (ACC), Execution Time (ET), and Computational Complexity (CC) demonstrates that the proposed model outperforms classical approaches in speed and efficiency. Simulations using IBM Qiskit and TensorFlow Quantum (TFQ) validate the effectiveness of this framework. The proposed approach provides a scalable and optimized solution for real-time telecommunication network traffic management, suggesting the technique for future development in quantum-enhanced AI applications.
The research article presents a Quantum-Enhanced Hybrid Machine Learning Model (QEHMLM) designed to accelerate data classification in Large-Scale Big Data Environments (LSBDE). The proposed framework integrates Quantum Computing (QC) with Classical Machine Learning Techniques (CMLT) including Support Vector Machine (SVM), Random Forest (RF), and Neural Network (Artificial-ANN) to overcome the computational and scalability limitations of classical approaches when processing High-Dimensional Datasets (HDD). The model leverages quantum feature encoding, entanglement-based transformations, and classical optimization to enhance accuracy and convergence speed. Simulations conducted using IBM Qiskit and TensorFlow Quantum on the Mobile Telecom Network Traffic Dataset validate the industrial applicability of the suggested approach in Applied Operational environment. The QEHMLM achieves notable improvements, recording 97% accuracy, 95% F1-score, and nearly 20% reduction in computational time compared to classical counterparts. These outcomes confirm that Hybrid Quantum-Classical Systems (HQCS) provide a scalable, efficient, and high-performance paradigm for next-generation Big Data analytics and intelligent computational ecosystems.
This work investigates a hybrid quantum-classical machine learning methodology that combines deep learning with quantum computing to improve predictive analytics. The method starts by changing basic input features into the best mixed forms using classical weight matrices and quantum state encoding. A unitary operator refines quantum states to enhance feature extraction and representation. Gradient-based alterations repeatedly modify feature weights during optimization to improve accuracy. Quantum circuits not only change data but also enhance system separability and resilience. Normalizing and enhancing extracted properties using quantum and classical transformations ensures stability and consistency. Performance trials demonstrate that the hybrid technique improves accuracy, precision, recall, computational complexity, execution time, and energy economy. The hybrid model always outperforms machine learning, deep learning, and quantum mechanics-based approaches. The technique is now more scalable, making enormous data sets easier to analyze. Faster convergence rates and noise tolerance improve model performance in real-world applications. The findings suggest that quantum advances and classical learning approaches improve model interpretability and decision-making reliability. The research found hybrid quantum-classical machine learning to be a powerful, scalable, and efficient predictive modeling solution. This method improves precision while conserving computing power. By using both paradigms, the technique provides deeper insights and better predictions. As a major facilitator for future AI-driven applications, the hybrid model provides a possible solution for difficult machine learning challenges. These developments make hybrid models available.
Background: The emergent need for scalable artificial intelligence (AI) models emphasizes the shortcomings of traditional machine learning in dealing with big, complicated datasets owing to computation and energy limitations. Objective: The architecture takes advantage of quantum encoding methods like amplitude and angle encoding for compact feature representation, with classical preprocessing providing compatibility and stability. Methods: To overcome these issues, this research introduces a Hybrid Quantum-Classical Machine Learning (HQML) framework that combines Quantum Variational Classifiers (QVCs) and Quantum Convolutional Neural Networks (QCNNs) with classical deep learning layers. Results: Experiments were performed on pre-curated datasets taken from the Scikit Machine Learning Repository, with hybrid models compared to classical Convolutional Neural Networks (CNNs). The implementation was done with PennyLane using TensorFlow Quantum for compatibility with noisy intermediate-scale quantum (NISQ) machines. The comparative analysis identified that the suggested HQML model achieves significant performance gains, as classification accuracy increased from 73 percent in the baseline classical CNN to 98% in the hybrid model. Precision also went up from 77 to 96 percent, recall from 81 to 100 percent, and F1-score from 79 to 98 percent. These findings confirm the ability of the model to leverage quantum benefits for extracting complicated data distributions without losing stability through classical components. The findings confirm the viability of HQML to real-world finance, healthcare, and intelligent automation applications where scalability and precision matter. Conclusion: This research establishes that quantum-classical integration not only minimizes training overhead but also attains near-perfect classification efficiency with constrained qubit resources. The research aids in filling the gap between theoretical quantum potential and deployable practical AI, opening the door for next-generation quantum-boosted intelligence.
Network traffic analysis is essential for modern communication systems, focusing on tasks like traffic classification, prediction, and anomaly detection. While classical Machine Learning (ML) and Deep Learning (DL) methods have proven effective, their scalability and real-time performance can be limited by evolving traffic patterns and computational demands. Quantum Machine-Learning (QML) offers a promising alternative by utilizing quantum computing's parallelism. This paper examines QML's application in mobile traffic classification, comparing classical methods such as Multi-layer Perceptron (MLP) and Convolutional Neural Networks (CNNs) with Quantum Neural Networks (QNNs) using different embedding types. Our experiments, conducted on the MIRAGE-COVID-CCMA-2022 dataset, show that QNNs achieve competitive performance, indicating QML's potential for efficient large-scale traffic classification in future networks.
Quantum computers are the latest dream of humans in solving nondeterministic polynomial problems. These problems include machine learning and object classification. However, commercial quantum computers are still not available. In the meantime, scientists developed hybrid models by combining Convolutional Neural Networks (CNN) and Quantum Neural Networks (QNN) to apply quantum computing to artificial intelligence (AI) applications. The hybrid model accelerates the processing of an AI machine by combining the advantage of CNN in feature extraction with the parallel computing capability of QNN. In this study, we optimized the existing hybrid models with a fewer number of qubits to improve the performance of QNN on digit recognition. The study also presents the implementation of the model using Qiskit, describes the training process, and analyzes the impact of the number of qubits and quantum gates on the classification results. Experiments show that Hybrid Quantum-Classical Neural Networks (H-QNN) have the potential to outperform pure CNN. Finally, the study suggests future improvements, including the optimization of quantum algorithms and extending experiments to more complex data sets.
Quantum Machine Learning signifies a possible collaboration of quantum computing and artificial intelligence, presenting potential benefits in addressing intricate computational challenges. Hybrid quantum-classical neural networks (QNNs) have developed as a formidable instrument for classification tasks, utilizing quantum feature encoding and classical optimization. This research employs a hybrid Quantum Neural Network framework utilizing Google Cirq, Classical data is encoded into quantum states via Z, ZZ, Pauli, and hybrid feature maps to use the distinctive attributes of quantum processing in representing intricate interactions among characteristics. The hybrid model combines quantum circuits for feature extraction with classical neural networks for training and assessment. The performance metrics—accuracy, precision, recall, and F1-score—demonstrate that the hybrid feature map method regularly surpasses individual feature maps, especially in multi-class and binary classification problems. However, when benchmarked against classical deep learning models such as Convolutional Neural Networks (CNNs), the CNN achieved superior results in classification accuracy and efficiency. This underscores the current limitations of quantum models while highlighting their potential for further advancements.