  Quantum machine learning is a discipline that holds the promise of
revolutionizing data processing and problem-solving. However, dissipation and
noise arising from the coupling with the environment are commonly perceived as
major obstacles to its practical exploitation, as they impact the coherence and
performance of the utilized quantum devices. Significant efforts have been
dedicated to mitigate and control their negative effects on these devices. This
Perspective takes a different approach, aiming to harness the potential of
noise and dissipation instead of combatting them. Surprisingly, it is shown
that these seemingly detrimental factors can provide substantial advantages in
the operation of quantum machine learning algorithms under certain
circumstances. Exploring and understanding the implications of adapting quantum
machine learning algorithms to open quantum systems opens up pathways for
devising strategies that effectively leverage noise and dissipation. The recent
works analyzed in this Perspective represent only initial steps towards
uncovering other potential hidden benefits that dissipation and noise may
offer. As exploration in this field continues, significant discoveries are
anticipated that could reshape the future of quantum computing.
