Quantum Machine Learning Algorithms for Pattern Recognition in Video Sequences

The integration of quantum computing principles with machine learning algorithms presents a paradigm shift in addressing complex pattern recognition challenges in video sequences. This research investigates the application of quantum machine learning (QML) algorithms for temporal pattern recognition in video data, comparing their performance against classical approaches across multiple benchmarks. We implement and evaluate quantum neural networks (QNNs), quantum support vector machines (QSVMs), and hybrid quantum-classical architectures using the IBM Quantum Experience platform and Qiskit framework. Our experimental evaluation, conducted on standardized video datasets including UCF-101 and HMDB-51, demonstrates that while quantum algorithms show promising theoretical advantages in feature space dimensionality and processing parallelism, their practical performance remains constrained by current hardware limitations and noise characteristics. Specifically, our hybrid quantum-classical approach achieved 82.7% accuracy on video action recognition tasks, compared to 87.3% for classical SVMs, with quantum advantages emerging in scenarios requiring high-dimensional feature processing. This work contributes to the understanding of quantum computing's potential in multimedia analysis and provides empirical evidence for future quantum machine learning research directions. Keywords: quantum machine learning, pattern recognition, video analysis, quantum neural networks, quantumcomputing