Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.03113 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908808114077696 |
|---|---|
| author | Wang, Tie-Jun Zhang, Run-Qing Qian, Ling Song, Yun-Tao Lan, Ting Liu, Hai-Qing Li, Keren |
| author_facet | Wang, Tie-Jun Zhang, Run-Qing Qian, Ling Song, Yun-Tao Lan, Ting Liu, Hai-Qing Li, Keren |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_03113 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Validating a Koopman-Quantum Hybrid Paradigm for Diagnostic Denoising of Fusion Devices Wang, Tie-Jun Zhang, Run-Qing Qian, Ling Song, Yun-Tao Lan, Ting Liu, Hai-Qing Li, Keren Quantum Physics 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. |
| title | Validating a Koopman-Quantum Hybrid Paradigm for Diagnostic Denoising of Fusion Devices |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2602.03113 |