Lean Quantum Intelligence: A Compact Variational-Circuit Hybrid for Turbofan RUL Prediction with SHAP Attribution

Remaining useful life (RUL) prediction is central to prognostics and health management of turbomachinery, but deep models are often large and opaque. We re-evaluate a compact quantum-classical hybrid (a 1D convolutional encoder, a four-qubit variational circuit with eight trainable angles and a small regression head; 8237 parameters) on all four NASA C-MAPSS sub-datasets. Validation uses held-out engines instead of held-out windows, which we show leaks information, and results are averaged over several seeds. Controls without the circuit, a frozen random circuit, parameter-matched LSTM and GRU models and ridge regression are included. The hybrid reaches mean test RMSE values of 19.05, 20.78, 18.50 and 30.82 cycles on FD001–FD004 (19.16 and 25.88 on FD002 and FD004 with condition-wise normalisation). It is indistinguishable from the classical controls on FD001–FD003 and worse on FD004, and matched-size recurrent models or ridge regression are better on single-condition data. Simulated finite-shot sampling with 256 shots costs about 0.3 cycles. SHAP attribution points to compressor-path sensors, with moderate rank stability across seeds. The study provides a controlled testbed rather than evidence of quantum advantage.