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Quantum Machine Learning is where nowadays machine learning meets quantuminformation science. In order to implement this new paradigm for novel quantumtechnologies, we still need a much deeper understanding of its underlyingmechanisms, before proposing new algorithms to feasibly address real problems.In this context, quantum generative adversarial learning is a promisingstrategy to use quantum devices for quantum estimation or generative machinelearning tasks. However, the convergence behaviours of its training process,which is crucial for its practical implementation on quantum processors, havenot been investigated in detail yet. Indeed here we show how different trainingproblems may occur during the optimization process, such as the emergence oflimit cycles. The latter may remarkably extend the convergence time in thescenario of mixed quantum states playing a crucial role in the alreadyavailable noisy intermediate scale quantum devices. Then, we propose newstrategies to achieve a faster convergence in any operating regime. Our resultspave the way for new experimental demonstrations of such hybridclassical-quantum protocols allowing to evaluate the potential advantages overtheir classical counterparts.

How to enhance quantum generative adversarial learning of noisy information