A Simulation-Based Machine Learning Framework for Quantum Noise-Channel Classification

Quantum teleportation (QT) is the most essential component of quantum communication, but its performance falls significantly when noisy intermediate-scale quantum (NISQ) channels are present. This paper presents a physics-informed machine learning (ML) framework for classifying quantum noise channels in teleportation systems. We employ Kraus operators to model five representative noise processes: depolarizing, amplitude damping, phase damping, bit flip, and phase flip. Additionally, we simulate their effects on teleportation fidelity and entanglement. A dataset comprising 15,360 instances is generated using the Monte Carlo method, featuring 35-dimensional vectors derived from quantum information metrics, including fidelity, entropy, purity, and Quantum Fisher Information (QFI). Multiple classifiers are evaluated, including Support Vector Machines (SVM), Random Forest (RF), Multilayer Perceptron (MLP), and Gradient Boosting (GB). The results indicate that ensemble methods demonstrate superior performance compared to alternative models. Specifically, GB attained the highest accuracy of 96.5%, ensuring strong per-class performance across all categories of errors. These findings highlight the potential of physics-informed ML for developing noise-aware and reliable quantum communication systems.