Learning Phase-Flip Noise Using Balanced Datasets and Hybrid Models for Noise Classification in Quantum Circuits

Quantum computing promises unparalleled computational power, but its practical implementation is hampered by quantum noise, which affects the reliability of quantum circuits. This paper addresses the critical challenge of noise classification in NISQ ‘Noisy Intermediate-Scale Quantum’ algorithms, focusing on bit flip, phase flip, and depolarising noise. We introduce a novel balanced quantum noise dataset generated through random quantum circuits, ensuring comprehensive coverage of various noise types and intensities. Additionally, we develop a hybrid quantum-classical machine learning model that achieves over 85% accuracy in classifying bit flip and depolarising noise, and perfect accuracy in detecting phase flip noise under our simulation conditions. Our approach builds on a dual quantum architecture, utilising IBM Qiskit for dataset generation and Pennylane for model training. These simulation results indicate potential improvements for noise-aware modelling and error mitigation; however, we emphasise that translating this performance to physical devices requires further validation due to hardware-specific decoherence and calibration effects.