Artificial spiking neural networks have found applications in areas where the temporal nature of activation offers an advantage, such as time series prediction and signal processing. To improve their efficiency, spiking architectures often run on custom-designed neuromorphic hardware, but, despite their attractive properties, these implementations have been limited to digital systems. We describe an artificial quantum spiking neuron that relies on the dynamical evolution of two easy to implement Hamiltonians and subsequent local measurements. The architecture allows exploiting complex amplitudes and back-action from measurements to influence the input. This approach to learning protocols is advantageous in the case where the input and output of the system are both quantum states. We demonstrate this through the classification of Bell pairs which can be seen as a certification protocol. Stacking the introduced elementary building blocks into larger networks combines the spatiotemporal features of a spiking neural network with the non-local quantum correlations across the graph.

人工脉冲神经网络已发现在激活的时间性质具有优势的领域中应用，例如时间序列预测和信号处理。为了提高效率，尖峰体系结构通常在定制设计的神经形态硬件上运行，但是，尽管它们具有吸引人的特性，但这些实现方式仅限于数字系统。我们描述了一种人工量子加标神经元，它依赖于两个易于实现的哈密顿量和随后的局部测量的动力学演化。该架构允许利用复杂的幅度和来自测量的反作用来影响输入。在系统的输入和输出均为量子状态的情况下，这种学习协议的方法是有利的。我们通过贝尔对的分类证明了这一点，可以将其视为认证协议。将引入的基本构造块堆叠到较大的网络中，将尖峰神经网络的时空特征与整个图上的非局部量子相关性结合在一起。