光子时间延迟量子极端学习机
Photonic Time-Delayed Quantum Extreme Learning Machine
浏览论文内容
中文总结 AI 辅助
提出并数值模拟一种基于时间延迟非线性干涉仪的光子量子极端学习机，通过非线性双月基准二分类验证其计算能力，并证明其在损耗下通过权重重分配保持性能，具有实验可行性。
中文摘要 AI 辅助
我们提出并数值模拟了一种基于光子数分辨探测的时间延迟非线性干涉仪中非线性相互作用的光子量子极端学习机（QELM）。我们通过对非线性双月基准的二分类，从理论上证明了该储层的计算能力。对学习到的输出权重和替代量子读出的分析表明，基于光子数关联的紧凑特征集在保持分类精度的同时，可能降低测量复杂度。我们进一步表明，QELM在损耗影响下通过将学习到的权重自然重新分配到更大的一组可用特征上，维持其分类性能。这些结果证明了时间延迟光子实现的鲁棒性，并突显其作为实验上可访问的QELM平台的潜力。
英文摘要
We propose and numerically simulate a photonic quantum extreme learning machine (QELM) based on non-linear interactions in a time-delay non-linear interferometer with photon-number-resolving detection. We theoretically demonstrate the reservoir's computational capability by binary classification of the non-linear two moons benchmark. Analysis of the learned output weights and alternative quantum readouts shows that compact feature sets based on photon-number correlations preserve classification accuracy while potentially reducing measurement complexity. We further show that the QELM maintains its classification performance under the effect of losses by naturally redistributing the learned weights across a larger set of available features. These results demonstrate the resilience of the time-delay photonic implementation and highlight its potential as an experimentally accessible QELM platform.
发表机构
- Technical University of Denmark（丹麦技术大学）
机构由 AI 辅助整理，请以论文原文为准。
