Quantum-enhanced LSTM for predictive maintenance in industrial IoT systems

Abstract

An innovative solution for predictive maintenance in IIoT systems combining quantum computing with the proficiency of LSTM neural networks is proposed by us. Our concept is guided by a hybrid quantum-classical architecture to facilitate quantum computing to exploit high-dimensional industrial sensor measurements while preserving crucial temporal relationships through particular quantum channels. Through the combination of the representational ingenuity of quantum circuits, along with the sequence-based modelling of classical LSTMs, QE-LSTM is uniquely positioned to handle complicated time series coming out of industrial sensors. At the heart of our methodology are the following unique elements:

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  A collaborative framework integrating quantum and classical technologies allowing for the quantum computer to manage the complex analysis of high dimensional sensor data in the industry.
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  Quantum channel designs were aimed at minimizing temporal dependencies in temporal series industrial measurements, thereby maximizing the quality of sequential analysis.
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  Under ODS hindcasting, QE-LSTM improved F1 by 4–5 percentage points on SECOM and reduced RMSE and NASA Score on C-MAPSS; trends were consistent on IMMD (Table 1, Table 2).

  Table 1. Performance comparison across datasets.

  Dataset	Model	Accuracy	Precision	Recall	F1	AUC
  SECOM	LSTM	0.864	0.842	0.809	0.825	0.902
  	CNN-LSTM	0.878	0.862	0.824	0.842	0.914
  	QE-LSTM (sim)	0.904	0.892	0.861	0.876	0.938
  	QE-LSTM (hardware)	0.896	0.881	0.850	0.865	0.930
  IMMD	LSTM	0.906	0.883	0.862	0.872	0.943
  	CNN-LSTM	0.913	0.891	0.869	0.880	0.949
  	QE-LSTM (sim)	0.928	0.908	0.888	0.898	0.960

  QE-LSTM (sim) vs LSTM F1 deltas: SECOM +5.1 pp, IMMD +2.6 pp; paired t-test p < 0.01.

  Table 2. RUL prediction performance metrics.

  Metric	Classical LSTM	CNN-LSTM	QE-LSTM (sim)	QE-LSTM (hardware)	Improvement vs LSTM
  RMSE ↓	20.6	19.4	18.1	18.7	12.1 %
  MAE ↓	15.4	14.6	13.8	14.3	10.4 %
  NASA Score ↓	692	648	603	621	12.8 %

  * Evaluations are positive if all the metrics have smaller values. Score is a metric that stands out because of the tendency to heavily penalise late predictions (which is highly relevant to effective maintenance planning).

In the application of failure detection of bearing, QE-LSTM improves F1 over classical baselines on SECOM by 4–5 pp, with similar gains on IMMD; results on C-MAPSS (RUL) show consistent reductions in RMSE and NASA score.