In this research, a comparative study of four Quantum Machine Learning (QML) models was conducted for fraud detection in finance. We proved that the Quantum Support Vector Classifier model achieved the highest performance, with F1 scores of 0.98 for fraud and non-fraud classes. Other models like the Variational Quantum Classifier, Estimator Quantum Neural Network (QNN), and Sampler QNN demonstrate promising results, propelling the potential of QML classification for financial applications. While they exhibit certain limitations, the insights attained pave the way for future enhancements and optimisation strategies. However, challenges exist, including the need for more efficient Quantum algorithms and larger and more complex datasets. The article provides solutions to overcome current limitations and contributes new insights to the field of Quantum Machine Learning in fraud detection, with important implications for its future development.

翻译：本研究对四种量子机器学习（QML）模型在金融欺诈检测中的表现进行了比较分析。实验证明，量子支持向量分类器模型取得了最优性能，其欺诈与非欺诈类别的F1分数均达到0.98。变分量子分类器、估计量子神经网络（QNN）和采样量子神经网络等其他模型也展现出具有前景的结果，彰显了QML分类在金融应用领域的潜力。尽管这些模型存在一定局限性，但研究所获得的洞察为未来的改进与优化策略奠定了基础。然而，当前仍面临挑战，包括需要更高效的量子算法以及更大规模、更复杂的数据集。本文提出了克服现有局限性的解决方案，为量子机器学习在欺诈检测领域贡献了新的见解，对其未来发展具有重要意义。