Noise influence on Quantum Machine Learning models' performance

We analyse how the training and performance of VQC models is affected by noise inherent to NISQ devices. In particular, we study the influence of three different types of quantum hardware noise: measurement errors, single qubit gate errors, and two-qubit gate errors (e.g., CNOT gate). Furthermore, we train the previously mentioned QML algorithms using noise models that emulate the behaviour of available quantum computers with high accuracy. We conclude that the tested QML models are suitable for operation on current NISQ devices.