NEWS    MERL's Quantum Machine Learning Technology Featured in Mitsubishi Electric Corporation Press Release

Date:

December 2, 2022

Description:

Mitsubishi Electric Corporation announced its development of a quantum artificial intelligence (AI) technology that automatically optimizes inference models to downsize the scale of computation with quantum neural networks. The new quantum AI technology can be integrated with classical machine learning frameworks for diverse solutions.

Mitsubishi Electric has confirmed that the technology can be incorporated in the world's first applications for terahertz (THz) imaging, Wi-Fi indoor monitoring, compressed sensing, and brain-computer interfaces. The technology is based on recent research by MERL's Connectivity & Information Processing team and Computational Sensing team.

Mitsubishi Electric's new quantum machine learning (QML) technology realizes compact inference models by fully exploiting the enormous capacity of quantum computers to express exponentially larger-state space with the number of quantum bits (qubits). In a hybrid combination of both quantum and classical AI, the technology can compensate for limitations of classical AI to achieve superior performance while significantly downsizing the scale of AI models, even when using limited data.

External Link:

https://us.mitsubishielectric.com/en/news/releases/global/2022/1202-a/index.html

Research Areas:

Artificial Intelligence, Computational Sensing, Machine Learning, Signal Processing, Human-Computer Interaction

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  BibTeX TR2022-121 PDF Video Presentation

  • @inproceedings{Koike-Akino2022sep,
  • author = {Koike-Akino, Toshiaki and Wang, Ye},
  • title = {quEEGNet: Quantum AI for Biosignal Processing},
  • booktitle = {IEEE Conference on Biomedical and Health Informatics (BHI)},
  • year = 2022,
  • month = sep,
  • publisher = {IEEE},
  • doi = {10.1109/BHI56158.2022.9926814},
  • issn = {2641-3604},
  • isbn = {978-1-6654-8791-7},
  • url = {https://www.merl.com/publications/TR2022-121}
  • }

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  BibTeX TR2022-110 PDF Video Presentation

  • @inproceedings{Koike-Akino2022aug,
  • author = {Koike-Akino, Toshiaki and Wang, Pu and Yamashita, Genki and Tsujita, Wataru and Nakajima, M.},
  • title = {Quantum Feature Extraction for THz Multi-Layer Imaging},
  • booktitle = {International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz)},
  • year = 2022,
  • month = aug,
  • publisher = {IEEE},
  • doi = {10.1109/IRMMW-THz50927.2022.9896037},
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  • url = {https://www.merl.com/publications/TR2022-110}
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  BibTeX TR2022-068 PDF Video Presentation

  • @inproceedings{Koike-Akino2022jun,
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  BibTeX TR2022-052 PDF Video Presentation

  • @inproceedings{Liu2022may3,
  • author = {Liu, Bryan and Koike-Akino, Toshiaki and Wang, Ye and Parsons, Kieran},
  • title = {Variational Quantum Compressed Sensing for Joint User and Channel State Acquisition in Grant-Free Device Access Systems},
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  • year = 2022,
  • month = may,
  • publisher = {IEEE},
  • doi = {10.1109/ICC45855.2022.9838445},
  • issn = {1938-1883},
  • isbn = {978-1-5386-8347-7},
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  BibTeX TR2022-044 PDF Video Presentation

  • @inproceedings{Koike-Akino2022may2,
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  • booktitle = {IEEE International Conference on Communications (ICC)},
  • year = 2022,
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  • doi = {10.1109/ICC45855.2022.9839011},
  • issn = {1938-1883},
  • isbn = {978-1-5386-8347-7},
  • url = {https://www.merl.com/publications/TR2022-044}
  • }