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

Date released: December 13, 2022


  •  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

  • MERL Contacts:
  • Research Areas:

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

    •  Koike-Akino, T., Wang, Y., "quEEGNet: Quantum AI for Biosignal Processing", IEEE Conference on Biomedical and Health Informatics (BHI), September 2022.
      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,
      • url = {https://www.merl.com/publications/TR2022-121}
      • }
    •  Koike-Akino, T., Wang, P., Yamashita, G., Tsujita, W., Nakajima, M., "Quantum Feature Extraction for THz Multi-Layer Imaging", International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), DOI: 10.1109/​IRMMW-THz50927.2022.9896037, August 2022.
      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},
      • issn = {2162-2035},
      • isbn = {978-1-7281-9427-1},
      • url = {https://www.merl.com/publications/TR2022-110}
      • }
    •  Koike-Akino, T., Wang, P., Wang, Y., "AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications", IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), DOI: 10.48550/​arXiv.2205.09115, June 2022.
      BibTeX TR2022-068 PDF Video Presentation
      • @inproceedings{Koike-Akino2022jun,
      • author = {Koike-Akino, Toshiaki and Wang, Pu and Wang, Ye},
      • title = {AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications},
      • booktitle = {IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)},
      • year = 2022,
      • month = jun,
      • doi = {10.48550/arXiv.2205.09115},
      • url = {https://www.merl.com/publications/TR2022-068}
      • }
    •  Liu, B., Koike-Akino, T., Wang, Y., Parsons, K., "Variational Quantum Compressed Sensing for Joint User and Channel State Acquisition in Grant-Free Device Access Systems", IEEE International Conference on Communications (ICC), DOI: 10.1109/​ICC45855.2022.9838445, May 2022.
      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},
      • booktitle = {IEEE International Conference on Communications (ICC)},
      • year = 2022,
      • month = may,
      • publisher = {IEEE},
      • doi = {10.1109/ICC45855.2022.9838445},
      • issn = {1938-1883},
      • isbn = {978-1-5386-8347-7},
      • url = {https://www.merl.com/publications/TR2022-052}
      • }
    •  Koike-Akino, T., Pu, W., Wang, Y., "Quantum Transfer Learning for Wi-Fi Sensing", IEEE International Conference on Communications (ICC), DOI: 10.1109/​ICC45855.2022.9839011, May 2022.
      BibTeX TR2022-044 PDF Video Presentation
      • @inproceedings{Koike-Akino2022may2,
      • author = {Koike-Akino, Toshiaki and Pu, Wang and Wang, Ye},
      • title = {Quantum Transfer Learning for Wi-Fi Sensing},
      • booktitle = {IEEE International Conference on Communications (ICC)},
      • year = 2022,
      • month = may,
      • doi = {10.1109/ICC45855.2022.9839011},
      • issn = {1938-1883},
      • isbn = {978-1-5386-8347-7},
      • url = {https://www.merl.com/publications/TR2022-044}
      • }