Pattern Recognition Using Quantum Machine Learning: An Innovative Approach Based on Quantum Neural Networks

This paper presents a novel approach to pattern recognition using quantum machine learning, offering substantial advancements over traditional and existing quantum models. The method leverages Quantum Neural Networks (QNNs), designed to recognize complex data features with unprecedented accuracy. We begin by outlining fundamental principles of quantum machine learning, focusing on its unique potential for addressing computational challenges in high-dimensional data analysis. A key component of our approach involves the integration of quantum states and quantum gates, which enable the QNN to capture intricate relationships within the data that classical models often overlook. In developing this QNN model, we simulate quantum data states and implement the model using quantum computing frameworks, such as Qiskit and TensorFlow Quantum. Experimental evaluations, conducted using both simulated quantum data and classical benchmark datasets, show that the proposed QNN achieves superior accuracy and speed compared to traditional neural networks and previous quantum-based models. Additionally, our approach demonstrates robustness in handling complex datasets, maintaining high performance even in cases of noisy data or limited training samples. The results highlight the effectiveness of quantum models in enhancing pattern recognition capabilities, showing potential applications in areas requiring rapid, accurate analysis of complex data patterns, such as financial modeling, cybersecurity, and medical diagnostics. By surpassing existing methods in both accuracy and efficiency, this study establishes a promising foundation for future research and practical applications in quantum-enhanced machine learning and pattern recognition.

کلیدواژه ها:

Quantum Neural Networks (QNN) ، Support Vector Machines ، Quantum Machine Learning ، Comparative Analysis ، Classical vs. Quantum Models