  Quantum computing promises a disruptive impact on machine learning
algorithms, taking advantage of the exponentially large Hilbert space
available. However, it is not clear how to scale quantum machine learning (QML)
to industrial-level applications. This paper investigates the scalability and
noise resilience of quantum generative learning applications. We consider the
training performance in the presence of statistical noise due to finite-shot
noise statistics and quantum noise due to decoherence to analyze the
scalability of QML methods. We employ rigorous benchmarking techniques to track
progress and identify challenges in scaling QML algorithms, and show how
characterization of QML systems can be accelerated, simplified, and made
reproducible when the QUARK framework is used. We show that QGANs are not as
affected by the curse of dimensionality as QCBMs and to which extent QCBMs are
resilient to noise.
