Enhancing movie recommendations using quantum support vector machine (QSVM).

The rising demand for high-quality movie recommendations in streaming services necessitates more efficient algorithms capable of handling large datasets. Traditional recommendation systems often struggle with long training times and high computational costs. This study introduces a novel movie recommendation system utilizing a quantum support vector machine (QSVM) to overcome these limitations. By leveraging quantum algorithms, QSVM enhances both the speed and accuracy of recommendations. Our approach involves collecting and preprocessing data, implementing classical SVM for baseline comparison, encoding data for QSVM, and executing QSVM using a publicly accessible IBM quantum computer. The results demonstrate that QSVM outperforms classical SVM, achieving a 96% accuracy and an F1 score of 0.9693, compared to the classical SVM’s 95.33% accuracy and 0.9641 F1 score. This signifies QSVM’s superior capability in handling complex datasets. Our findings highlight the potential of QSVM in movie recommendation systems, suggesting future research directions in quantum machine learning and its applications. [ABSTRACT FROM AUTHOR]