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%.

翻译：我们提出了一种基于激子-极化子凝聚体晶格的新型神经形态网络架构，该晶格通过非共振光学泵浦实现复杂互连与能量供给。网络采用二值化框架，每个神经元借助成对耦合凝聚体的空间相干性执行二值运算。这种源于极化子弹道传播的相干性确保了全网络的高效通信。由极化子激子组分驱动的非线性排斥作用所实现的神经元二值开关机制，相较于连续权重神经网络，在计算效率与可扩展性方面具有显著优势。我们的网络支持并行处理，相较于时序或脉冲编码的二值化系统，计算速度得到提升。通过手写数字识别的MNIST数据集评估系统性能，结果显示其预测分类准确率可达97.5%，展现出超越现有极化子神经形态系统的潜力。