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Updated: Jul 17, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Predicting premature failure of quantum cascade lasers with different quantum designs using active machine learning.

Ahmet Cagri Aydinkarahaliloglu1, Arifin Nur Alif1, Xiaojun Wang2

  • 1Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA.

Scientific Reports
|July 15, 2026
PubMed
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Machine learning predicts premature quantum cascade laser (QCL) failure, identifying 700% more devices than traditional methods. This approach enhances reliability and reduces ownership costs for critical laser applications.

Area of Science:

  • Optoelectronics
  • Materials Science
  • Artificial Intelligence

Background:

  • Mid-infrared quantum cascade lasers (QCLs) are vital for research, industry, and security.
  • Premature QCL failure, often within 400 hours, significantly increases operational costs.
  • Existing methods struggle to identify devices prone to early failure.

Purpose of the Study:

  • To develop machine learning (ML) algorithms for predicting premature failure in QCLs.
  • To improve the early identification of unreliable QCL devices.
  • To provide a framework for understanding QCL failure mechanisms.

Main Methods:

  • Support vector machine (SVM) algorithms were trained using QCL design and device properties.
  • The ML model was evaluated on its ability to predict early device failure.
Keywords:
Quantum cascade laserslaser reliabilitymachine learningpremature failure predictionquantum designssupport vector machines

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Last Updated: Jul 17, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

  • Model performance was assessed across different quantum designs and device parameters.

    • Main Results:

    • The SVM algorithm identified up to 700% more devices failing prematurely within the first 40 hours compared to conventional methods.
    • A single quantum design's training data was sufficient to predict the performance of lasers from a different quantum design.
    • High confidence in failure estimations was achieved.

    Conclusions:

    • ML-based prediction offers a significant improvement in identifying unreliable QCLs early.
    • The developed framework aids in ensuring device reliability and reducing ownership costs.
    • This approach provides valuable insights into the root causes of premature QCL failures.