Classification of Iris Dataset using Quantum Neural Networks

In this example, I developed a MATLAB implementation that employs quantum neural networks to classify the iris dataset. This demonstration serves as a practical case study illustrating the potential of quantum-inspired neural networks in solving classification problems. The implementation includes core components such as quantum state initialization through angle encoding, parameterized quantum circuit layers with rotational gates, and classical neural network integration for final classification. Through this code, you can learn to structure quantum-classical hybrid networks, configure training parameters like learning rate and circuit depth, and adapt the framework for other datasets. The modular design allows customization of quantum feature maps and classical optimization algorithms. This example provides hands-on experience in implementing quantum machine learning algorithms and can be extended for more complex classification tasks.