用光子量子分类器结合深度学习，实现聚合物光学带隙的高效分类。
Exploring polymer classification with a hybrid single-photon quantum approach
- 用深度神经网络提取聚合物特征，再交由光子量子分类器处理。
- 在真实量子处理器上实现聚合物分类，准确率接近经典模拟结果。
- 适合对量子计算与材料科学交叉应用感兴趣的科研人员。
聚合物具有复杂的结构和多样的性质，是化学与材料科学前沿研究的核心。传统计算方法，即使多尺度模拟也难以捕捉其复杂性，而量子计算为此提供了有前景的替代方案。尽管关于噪声中等规模量子（NISQ）设备实际效能仍存争议，我们提出一种混合经典-量子框架：先用经典深度神经网络进行聚合物特征提取，再通过基于单光子的量子分类器完成聚合物种类按光学带隙的分类。该流程在Quandela的Ascella量子处理器上实现，性能介于基于CPU的噪声模拟与原理验证实验之间。结果表明，该计算流程有效，且当前NISQ设备已可胜任与化学相关的分类任务。
原文摘要 · Abstract (English)
Polymers exhibit complex architectures and diverse properties that place them at the center of contemporary research in chemistry and materials science. As conventional computational techniques, even multi-scale ones, struggle to capture this complexity, quantum computing offers a promising alternative framework for extracting structure-property relationships. Noisy Intermediate-Scale Quantum (NISQ) devices are commonly used to explore the implementation of algorithms, including quantum neural networks for classification tasks, despite ongoing debate regarding their practical impact. We present a hybrid classical-quantum formalism that couples a classical deep neural network for polymer featurization with a single-photon-based quantum classifier native to photonic quantum computing. This pipeline successfully classifies polymer species by their optical gap, with performance in line between CPU-based noisy simulations and a proof-of-principle run on Quandela's Ascella quantum processor. These findings demonstrate the effectiveness of the proposed computational workflow and indicate that chemistryfrelated classification tasks can already be tackled under the constraints of today's NISQ devices.
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