硬件感知的量子注意力用于快速射电暴识别
Hardware-aware quantum attention for fast radio burst identification
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中文总结 AI 辅助
该研究开发了一个将原始望远镜数据转换为动态频谱片段、利用混合量子视觉Transformer识别快速射电暴的流水线，在FAST数据上达到94%准确率，并验证了量子硬件可行性。
中文摘要 AI 辅助
快速射电暴（FRB）是毫秒级的河外脉冲，其发现需要在巨大的信号参数空间中进行搜索，并排除由射频干扰和噪声主导的候选集。将量子处理器集成到FRB搜索中，需要一个将望远镜数据连接到硬件兼容模型的工作流程。在此，我们开发了一个流水线，将原始搜索模式PSRFITS数据转换为动态频谱片段，并使用混合量子视觉Transformer（QViT）识别类似FRB的信号。在标注的FAST数据上，所选QViT实现了94.00%的平均准确率和98.30%的召回率，与紧凑型经典视觉Transformer的性能相当。在一个未参与训练和模型选择的FRB源上，召回率达到97.11%。在82个片段上的真实设备执行产生了0.9161 ± 0.0074的平均模拟器-硬件Hellinger保真度。该工作流程将原始望远镜数据连接到量子辅助识别，并为评估在巡天规模部署之前必须解决的测量要求提供了实验基础。
英文摘要
Fast radio bursts (FRBs) are millisecond-duration extragalactic pulses whose discovery requires searches over large signal-parameter spaces and the rejection of candidate sets dominated by radio-frequency interference and noise. Integrating quantum processors into FRB searches requires a workflow connecting telescop data to hardware-compatible models. Here we develop a pipeline that converts raw search-mode PSRFITS data into dynamic-spectrum segments and identifies FRB-like signals using a hybrid Quantum Vision Transformer (QViT). On labelled FAST data, the selected QViT achieves a mean accuracy of 94.00% and recall of 98.30%, with similar performance to a compact classical Vision Transformer. Recall reaches 97.11% on an FRB source excluded from training and model selection. Real-device execution on 82 segments yields a mean simulator-hardware Hellinger fidelity of 0.9161 +/- 0.0074. The workflow connects raw telescope data to quantum-assisted identification and provides an experimental basis for assessing the measurement requirements that must be addressed before survey-scale deployment.
