  Variational quantum algorithms represent a powerful approach for solving
optimization problems on noisy quantum computers, with a broad spectrum of
potential applications ranging from chemistry to machine learning. However,
their performances in practical implementations crucially depend on the
effectiveness of quantum circuit training, which can be severely limited by
phenomena such as barren plateaus. While, in general, dissipation is
detrimental for quantum algorithms, and noise itself can actually induce barren
plateaus, here we describe how the inclusion of properly engineered Markovian
losses after each unitary quantum circuit layer can restore the trainability of
quantum models. We identify the required form of the dissipation processes and
establish that their optimization is efficient. We benchmark our proposal in
both a synthetic and a practical quantum chemistry example, demonstrating its
effectiveness and potential impact across different domains.
