Hybrid quantum recurrent neural network for remaining useful life prediction of turbofan engines

Excerpt

arXiv:2504.20823v3 Announce Type: replace Abstract: Accurate remaining useful life (RUL) estimation underpins safe operation and cost-effective maintenance of aerospace propulsion systems. We propose a Hybrid Quantum Recurrent Neural Network (HQRNN) for jet-engine RUL forecasting on the NASA C-MAPSS FD001 benchmark. The HQRNN stacks Quantum Long Short-Term Memory (QLSTM) layers, replacing each LSTM gate's linear transformation with a Quantum Depth-Infused (QDI) circuit; this is followed by class