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Mitsubishi Electric’s New Quantum Artificial Intelligence Technology Uses Automated Design to Realize Compact Inference Models
Rhea-AI Impact
(Low)
Rhea-AI Sentiment
(Neutral)
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Rhea-AI Summary
Mitsubishi Electric Corporation has announced its development of a groundbreaking quantum AI technology that optimizes inference models using quantum neural networks, thereby reducing computational scale. This innovative technology can be integrated with classical machine learning, leading to versatile applications. Notably, it will be used in the first-ever terahertz imaging application for non-destructive inspections. Other uses include indoor monitoring via Wi-Fi signals and biosignal processing. These advancements highlight Mitsubishi Electric's commitment to leveraging AI for diverse technological solutions.
Positive
Development of quantum AI technology for optimizing inference models.
Integration potential with classical machine learning frameworks.
First application in terahertz imaging for non-destructive inspections.
Negative
None.
Can be incorporated in first-ever application for terahertz imaging
TOKYO--(BUSINESS WIRE)--
Mitsubishi Electric Corporation (TOKYO:6503) announced today its development of a quantum artificial intelligence (AI) technology that automatically designs and 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 application for terahertz (THz) imaging using ultra-high-frequency waves to perform non-destructive inspections by exploiting properties such as the high penetration of radio waves and the high directivity of light waves. It also can be used for indoor monitoring in which Wi-Fi signals are used to observe room environments for human movements. Other potential applications also are possible, such as compressed sensing to retrieve original data from mixed measurement data and biosignal processing for brain-computer interfaces.