This blog explores Quantum Machine Learning (QML) and its potential in revolutionizing areas like drug discovery and finance. It covers quantum kernel methods and quantum neural networks, providing practical Python examples using Qiskit. The post also discusses the installation of Qiskit and the use of simulators or real hardware. The Quantum Kernel Methods section explains support vector machines (SVMs) and non-linear classifications using the kernel trick. The Quantum Kernel with ZZFeatureMaps example showcases the implementation of a quantum kernel for breast cancer classification. The Quantum Kernel Training section explores Quantum Kernel Alignment (QKA) for binary classification. The Quantum Neural Networks section explains the integration of QNNs into machine learning pipelines and demonstrates their usage with a breast cancer dataset. The conclusion highlights the importance of pre-processing techniques, such as feature rescaling, feature extraction, and feature selection, in optimizing QML models.
