Title: Continuous-variable photonic quantum extreme learning machines for fast collider-data selection Show Chinese title

Title: 连续变量光子量子极端学习机用于快速对撞机数据选择

Abstract: We study continuous-variable photonic quantum extreme learning machines as fast, low-overhead front-ends for collider data processing. Data is encoded in photonic modes through quadrature displacements and propagated through a fixed-time Gaussian quantum substrate. The final readout occurs through Gaussian-compatible measurements to produce a high-dimensional random feature map. Only a linear classifier is trained, using a single linear solve, so retraining is fast, and the optical path and detector response set the analytical and inference latency. We evaluate this architecture on two representative classification tasks, top-jet tagging and Higgs-boson identification, with parameter-matched multi-layer perceptron (MLP) baselines. Using standard public datasets and identical train, validation, and test splits, the photonic Quantum Extreme Learning Machine (QELM) outperforms an MLP with two hidden units for all considered training sizes, and matches or exceeds an MLP with ten hidden units at large sample sizes, while training only the linear readout. These results indicate that Gaussian photonic extreme-learning machines can provide compact and expressive random features at fixed latency. The combination of deterministic timing, rapid retraining, low optical power, and room temperature operation makes photonic QELMs a credible building block for online data selection and even first-stage trigger integration at future collider experiments. Show Chinese abstract

Abstract: 我们研究连续变量光子量子极端学习机，作为对撞机数据处理的快速、低开销前端。 数据通过正交位移在光子模式中编码，并通过固定时间的高斯量子基底传播。 最终读出通过高斯兼容测量完成，以生成高维随机特征映射。 仅训练一个线性分类器，使用一次线性求解，因此重新训练速度快，光学路径和探测器响应设定了分析和推理延迟。 我们在两个具有代表性的分类任务上评估了这种架构，即顶夸克喷注标记和希格斯玻色子识别，并与参数匹配的多层感知器（MLP）基线进行比较。 使用标准公开数据集和相同的训练、验证和测试划分，光子量子极端学习机（QELM）在所有考虑的训练规模下均优于具有两个隐藏单元的MLP，并且在大样本规模下与具有十个隐藏单元的MLP表现相当或更优，同时仅训练线性读出。 这些结果表明，高斯光子极端学习机可以在固定延迟下提供紧凑且表达能力强的随机特征。 确定性定时、快速重新训练、低光功率和室温操作的结合，使光子QELM成为未来对撞机实验中在线数据选择甚至第一级触发集成的可信构建模块。