基于激子-极化激元光学尖峰处理的图像识别
Image recognition based on optical spike processing with exciton-polaritons
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中文总结 AI 辅助
本文数值演示了利用激子-极化激元凝聚体的瞬态响应进行图像识别，通过光脉冲编码MNIST图像并结合线性分类器，在十类分类中达到93.2%的准确率，在7/9二分类中达97.3%，证明其可提取非线性特征用于超快光学处理。
中文摘要 AI 辅助
激子-极化激元凝聚体将超快动力学与强光学非线性相结合，使其有望用于光学神经形态计算。在此，我们数值演示了利用激子-极化激元凝聚体的瞬态响应进行图像识别。缩小的MNIST图像被编码为光脉冲序列，由此产生的凝聚体动力学被采样并由线性分类器处理。对于十类分类，凝聚体特征达到92.2%的准确率，而原始像素线性分类为91.4%。将两组特征结合后，准确率提升至93.2%。对于具有挑战性的数字7和9的二分类，组合模型达到97.3%，超过了线性（95.5%）和前馈神经网络（96.6%）基线。这些结果表明，极化激元动力学可以为超快光学信息处理提取有用的非线性特征。
英文摘要
Exciton-polariton condensates combine ultrafast dynamics with strong optical nonlinearities, making them promising for optical neuromorphic computing. Here, we numerically demonstrate image recognition using the transient response of an exciton-polariton condensate. Downscaled MNIST images are encoded as optical pulse sequences, and the resulting condensate dynamics are sampled and processed by a linear classifier. For ten-class classification, condensate features achieve 92.2% accuracy, compared with 91.4% for raw-pixel linear classification. Combining both feature sets increases the accuracy to 93.2%. For the challenging binary classification of digits 7 and 9, the combined model reaches 97.3%, exceeding both the linear (95.5%) and feed-forward neural-network (96.6%) baselines. These results demonstrate that polariton dynamics can extract useful nonlinear features for ultrafast optical information processing.
发表机构
- Faculty of Physics, University of Warsaw（华沙大学物理学院）
- Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw（华沙大学数学与计算建模交叉中心）
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