In this paper, we explore the power of Quantum Machine Learning as we extend,
implement and evaluate algorithms like Quantum Support Vector Classifier
(QSVC), Pegasos-QSVC, Variational Quantum Circuits (VQC), and Quantum Neural
Networks (QNN) in Qiskit with diverse feature mapping techniques for genomic
sequence classification.

本文探讨了量子机器学习的潜力，通过在 Qiskit 中利用多种特征映射技术对基因组序列分类，扩展、实现和评估了量子支持向量分类器（QSVC），Pegasos-QSVC，变分量子电路（VQC）和量子神经网络（QNN）等算法。

Qiskit 中基因组数据的量子机器学习算法的独立实现

An Independent Implementation of Quantum Machine Learning Algorithms in  Qiskit for Genomic Data

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.

对四个量子机器学习模型在金融欺诈检测中进行了比较研究，证明量子支持向量分类器模型在欺诈和非欺诈类别上取得了最高性能，F1 分数达到了 0.98。其他模型如变分量子分类器、估计量子神经网络（QNN）和采样器 QNN 展示了有前景的结果，推动了量子机器学习在金融应用中的潜力。虽然它们存在一定的限制，但所得到的见解为未来的改进和优化策略铺平了道路。然而，现存的挑战包括需求更高效的量子算法和更大更复杂的数据集。本文提供了克服当前限制的解决方案，并为量子机器学习在欺诈检测领域做出了新的贡献，对其未来发展具有重要意义。