验证一种Koopman-量子混合范式用于融合装置的诊断去噪
Validating a Koopman-Quantum Hybrid Paradigm for Diagnostic Denoising of Fusion Devices
AI总结：
本研究提出一种Koopman-量子混合范式，用于融合装置诊断数据去噪，通过物理指导的Koopman算子压缩数据并结合量子神经网络，实现高精度去噪与低参数消耗。
摘要：
量子机器学习（QML）在数据密集型科学中的潜力受到将高维、混沌的经典数据接口到资源受限、噪声量子处理器的困难的严格限制。为弥合这一差距，我们引入了一种物理指导的Koopman-量子混合框架，其理论基础是我们在Koopman算子（线性化非线性动力学）和量子演化之间建立的表示层结构同构性。基于这一理论基础，我们设计了一个可实现的NISQ友好管道：Koopman算子作为物理意识的“数据蒸馏器”，将波形压缩成紧凑的“量子准备”特征，随后由模块化、并行的量子神经网络处理。我们在此框架上验证了4,763个标记的通道序列，来自433次托卡马克系统的放电。结果表明，我们的模型在筛选损坏的诊断数据方面达到了97.0%的准确率，性能与最先进的深度经典CNNs相当，但使用了数量级更少的可训练参数。这项工作建立了一种实用的、基于物理的范式，用于在受限环境中利用量子处理，为量子增强的边缘计算提供了可扩展的路径。
英文摘要：
The potential of Quantum Machine Learning (QML) in data-intensive science is strictly bottlenecked the difficulty of interfacing high-dimensional, chaotic classical data into resource-limited, noisy quantum processors. To bridge this gap, we introduce a physics-informed Koopman-Quantum hybrid framework, theoretically grounded in a representation-level structural isomorphism we establish between the Koopman operator, which linearizes nonlinear dynamics, and quantum evolution. Based on this theoretical foundation, we design a realizable NISQ-friendly pipeline: the Koopman operator functions as a physics-aware "data distiller," compressing waveforms into compact, "quantum-ready" features, which are subsequently processed by a modular, parallel quantum neural network. We validated this framework on 4,763 labeled channel sequences from 433 discharges of the tokamak system. The results demonstrate that our model achieves 97.0\% accuracy in screening corrupted diagnostic data, matching the performance of state-of-the-art deep classical CNNs while using orders-of-magnitude fewer trainable parameters. This work establishes a practical, physics-grounded paradigm for leveraging quantum processing in constrained environments, offering a scalable path for quantum-enhanced edge computing.
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