论文标题
通过相对论启发的量子动力学的机器学习
Machine learning via relativity-inspired quantum dynamics
论文作者
论文摘要
我们根据量子系统的相对论动力学（即腔谐振器内的量子检测器）提出了一种机器学习方案。例如，可以在电路QED平台中实现等效模型模型。我们考虑了一种储层计算方案，其中输入数据嵌入了系统的调制中（相当于相对论对象的加速度），并且通过测量可观察到的可观察到的线性组合获得了输出数据。作为一个说明性的例子，我们为具有挑战性的分类任务模拟了这种相对论的量子机，显示了相对论制度中准确性的很大提高。使用内核机理论，我们表明，在相对论制度中，任务无关的表现性在牛顿政权方面被显着放大。
We present a machine-learning scheme based on the relativistic dynamics of a quantum system, namely a quantum detector inside a cavity resonator. An equivalent analog model can be realized for example in a circuit QED platform subject to properly modulated driving fields. We consider a reservoir-computing scheme where the input data are embedded in the modulation of the system (equivalent to the acceleration of the relativistic object) and the output data are obtained by linear combinations of measured observables. As an illustrative example, we have simulated such a relativistic quantum machine for a challenging classification task, showing a very large enhancement of the accuracy in the relativistic regime. Using kernel-machine theory, we show that in the relativistic regime the task-independent expressivity is dramatically magnified with respect to the Newtonian regime.
