Hybrid Quantum–Classical Machine Learning Architectures for Scalable Pattern Recognition in High-Dimensional and Noisy Data Environments
DOI:
https://doi.org/10.62802/sptecp98Keywords:
hybrid quantum–classical learning, pattern recognition, high-dimensional data, quantum machine learning, noisy data analytics, scalable AI architecturesAbstract
The exponential growth of high-dimensional data across scientific, financial, and technological domains has intensified the demand for scalable pattern recognition systems capable of operating under noise, uncertainty, and computational constraints. Classical machine learning techniques, while effective in many contexts, often encounter limitations related to dimensionality, feature sparsity, and optimization complexity. This paper explores hybrid quantum–classical machine learning architectures for scalable pattern recognition in high-dimensional and noisy data environments, emphasizing the synergistic integration of quantum computational principles with established classical algorithms. By leveraging quantum feature mapping, variational quantum circuits, and classical optimization frameworks, hybrid architectures aim to enhance representational capacity and computational efficiency. The study synthesizes recent advances in quantum machine learning and evaluates their potential to address scalability and robustness challenges in real-world data analytics. The findings suggest that hybrid quantum–classical systems may serve as a complementary paradigm that bridges theoretical quantum advantage with practical machine learning applications.
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