Dissipation induced by interactions with an external environment typically hinders the performance of quantum computation, but in some cases can be turned out as a useful resource. We show the potential enhancement induced by dissipation in the field of quantum reservoir computing introducing tunable local losses in spin network models. Our approach based on continuous dissipation is able not only to reproduce the dynamics of previous proposals of quantum reservoir computing, based on discontinuous erasing maps but also to enhance their performance. Control of the damping rates is shown to boost popular machine learning temporal tasks as the capability to linearly and non-linearly process the input history and to forecast chaotic series. Finally, we formally prove that, under non-restrictive conditions, our dissipative models form a universal class for reservoir computing. It means that considering our approach, it is possible to approximate any fading memory map with arbitrary precision.

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耗散作为量子储层计算的资源

与外部环境相互作用引起的耗散通常会阻碍量子计算的性能，但在某些情况下可以成为有用的资源。我们展示了量子存储计算领域中由耗散引起的潜在增强，在自旋网络模型中引入了可调局部损耗。我们基于连续耗散的方法不仅能够重现先前基于不连续擦除图的量子库计算提议的动态，而且还能够增强其性能。阻尼率的控制被证明可以促进流行的机器学习时间任务，因为它能够线性和非线性处理输入历史并预测混沌序列。最后，我们正式证明，在非限制性条件下，我们的耗散模型形成了油藏计算的通用类。这意味着考虑到我们的方法，可以以任意精度近似任何衰落内存映射。