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 allows for the trainability of quantum models. We identify the required form of the dissipation processes and establish that their optimization is efficient. We benchmark the generality of our proposal in both a synthetic and a practical quantum chemistry example, demonstrating its effectiveness and potential impact across different domains.

变分量子算法代表了解决噪声量子计算机上的优化问题的强大方法，具有从化学到机器学习的广泛潜在应用。然而，它们在实际实现中的性能关键取决于量子电路训练的有效性，而量子电路训练的有效性可能会受到贫瘠高原等现象的严重限制。虽然一般来说，耗散对于量子算法是有害的，并且噪声本身实际上会导致贫瘠的平台，但在这里我们描述了如何在每个单一量子电路层之后包含正确设计的马尔可夫损失，从而允许量子模型的可训练性。我们确定了耗散过程所需的形式，并确定其优化是有效的。我们在合成和实际量子化学示例中对我们建议的通用性进行了基准测试，证明了其在不同领域的有效性和潜在影响。