Noise-resilient feature mapping for quantum machine learning: a practical study on NISQ simulators

Quantum machine learning faces a big problem with noise in quantum computers. When quantum algorithms run on real devices or emulators with noise, accuracy becomes very bad compared to classical methods. This paper shows implementation of quantum classifiers that work better in noisy conditions using the Qiskit framework. The method combines three simple techniques. First technique compares different feature maps like ZZFeatureMap and PauliFeatureMap to find which one is more stable when noise exists. Second technique adds measurement error mitigation inside the training loop of the variational quantum classifier. Third technique uses a Qiskit transpiler for making circuits shorter which reduces noise effects. Testing happened on a Qiskit Aer simulator with a noise model taken from an IBM quantum computer. Three datasets used for testing-Iris data with 4 features, Wine data with 13 features, and a small part of breast cancer data. Results show that good feature map choice plus error mitigation give about 15% more accuracy than normal VQC in noisy environments. Circuit optimization makes the circuit 40% less deep than the original. The research demonstrates noise resilient quantum machine learning. Our Qiskit implementation achieves reproducible results while accurately modeling real quantum noise on classical simulators.