Validating a Koopman-Quantum Hybrid Paradigm for Diagnostic Denoising of Fusion Devices

TL;DR

This paper introduces a physics-informed Koopman-Quantum hybrid framework that effectively compresses and processes fusion diagnostic data using quantum neural networks, achieving high accuracy with fewer parameters.

Contribution

It establishes a theoretical link between the Koopman operator and quantum evolution, enabling a practical NISQ-friendly pipeline for fusion diagnostics.

Findings

Achieves 97.0% accuracy in diagnostic data screening.

Uses significantly fewer trainable parameters than classical CNNs.

Validates the approach on real tokamak data.

Abstract

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.