Engineered dissipation to mitigate barren plateaus

Variational quantum algorithms provide a promising solution to optimization problems on noisy quantum computers, with applications ranging from chemistry to machine learning. These algorithms rely on efficient quantum circuit training, which can be affected by problems such as barren plateaus. Our research has uncovered a new method, based on dissipation, to mitigate this problem. Despite the negative effects of dissipation and noise on quantum algorithms, we show that the systematic inclusion of engineered Markovian losses following each unitary quantum circuit layer enables efficient trainability of quantum variational models. We define the precise properties that these dissipative methods must exhibit and establish their efficient optimization. In addition to analytically proving the absence of barren plateaus, we have also numerically tested our theory in both a synthetic and a quantum chemistry example. The results indicate that our strategy can make substantial progress in various domains, highlighting its versatility.