Neuromorphic and quantum computing have recently emerged as promising paradigms for advancing artificial intelligence, each offering complementary strengths. Neuromorphic systems built on spiking neurons excel at processing time series data efficiently through sparse, event-driven computation, consuming energy only upon input events. Quantum computing, on the other hand, operates on state spaces that grow exponentially in dimension with the number of qubits -- as a consequence of tensor-product composition -- with quantum states admitting superposition across basis states and entanglement between subsystems. Hybrid approaches combining these paradigms have begun to show potential, but existing quantum spiking models have important limitations. Notably, they implement classical memory mechanisms on single qubits, requiring repeated measurements to estimate firing probabilities, while relying on conventional backpropagation for training. In this paper, we propose a novel stochastic quantum spiking (SQS) neuron model that addresses these challenges. The SQS neuron uses multi-qubit quantum circuits to realize a spiking unit with internal quantum memory, enabling event-driven probabilistic spike generation in a single shot during inference. Furthermore, we study networks of SQS neurons, dubbed SQS neural networks (SQSNN), and demonstrate that they can be trained via a hardware-friendly local learning rule, eliminating the need for global classical backpropagation. The proposed SQSNN model is shown via experiments with both conventional and neuromorphic datasets to improve over previous quantum spiking neural networks, as well as over classical counterparts, when fixing the overall number of trainable parameters.

翻译：神经形态计算与量子计算作为推动人工智能发展的两大新兴范式，各自展现出互补的优势。基于脉冲神经元的神经形态系统通过稀疏的事件驱动计算高效处理时间序列数据，仅在输入事件发生时消耗能量。另一方面，量子计算在状态空间上运行，其维度随量子比特数量呈指数增长——这是张量积组合的必然结果——量子态允许基态间的叠加以及子系统间的纠缠。结合这两种范式的混合方法已开始显现潜力，但现有的量子脉冲模型存在重要局限。值得注意的是，这些模型在单个量子比特上实现经典记忆机制，需要重复测量以估计发放概率，同时依赖传统的反向传播进行训练。本文提出一种新颖的随机量子脉冲（SQS）神经元模型以应对这些挑战。SQS神经元采用多量子比特量子电路实现具有内部量子记忆的脉冲单元，在推理过程中能够单次完成事件驱动的概率脉冲生成。此外，我们研究了由SQS神经元构成的网络（称为SQS神经网络，SQSNN），并证明其可通过硬件友好的局部学习规则进行训练，从而无需全局经典反向传播。通过在传统数据集与神经形态数据集上的实验表明，在可训练参数总量固定的条件下，所提出的SQSNN模型相比先前的量子脉冲神经网络以及经典对应模型均有所提升。