We introduce a novel neuromorphic network architecture based on a lattice of
exciton-polariton condensates, intricately interconnected and energized through
non-resonant optical pumping. The network employs a binary framework, where
each neuron, facilitated by the spatial coherence of pairwise coupled
condensates, performs binary operations. This coherence, emerging from the
ballistic propagation of polaritons, ensures efficient, network-wide
communication. The binary neuron switching mechanism, driven by the nonlinear
repulsion through the excitonic component of polaritons, offers computational
efficiency and scalability advantages over continuous weight neural networks.
Our network enables parallel processing, enhancing computational speed compared
to sequential or pulse-coded binary systems. The system's performance was
evaluated using the MNIST dataset for handwritten digit recognition, showcasing
the potential to outperform existing polaritonic neuromorphic systems, as
demonstrated by its impressive predicted classification accuracy of up to
97.5%.

我们介绍了一种基于激子 - 极化子凝聚体格点的新型神经形态网络架构，通过非共振光抽运进行精细相互连接和能量注入。该网络采用二进制框架，在每个神经元中，通过成对相互耦合凝聚体的空间相干性，执行二进制操作。这种相干性是由于极子的弹道传播而产生的，确保了高效的整个网络通信。通过激子成分的非线性排斥驱动的二进制神经元切换机制，使得与连续权重神经网络相比具有计算效率和可扩展性的优势。我们的网络实现了并行处理，提高了计算速度，相比于顺序或脉冲编码的二进制系统。我们使用 MNIST 手写数字数据集评估了系统的性能，展示了优于现有极子神经形态系统的潜力，其令人印象深刻的预测分类准确率高达 97.5%。