Machine learning contributes to better quantum error correction

Researchers from the RIKEN Center for Quantum Computing have used machine learning to perform error correction for quantum computers—a crucial step for making these devices practical—using an autonomous correction system ...

Machine learning enhances X-ray imaging of nanotextures

Using a combination of high-powered X-rays, phase-retrieval algorithms and machine learning, Cornell researchers revealed the intricate nanotextures in thin-film materials, offering scientists a new, streamlined approach ...

Deep learning for quantum sensing

Quantum sensing represents one of the most promising applications of quantum technologies, with the aim of using quantum resources to improve measurement sensitivity. In particular, sensing of optical phases is one of the ...

New chemistry toolkit speeds analyses of molecules in solution

A new open-source toolkit automates the process of computing molecular properties in the solution phase, clearing new pathways for artificial-intelligence design and discovery in chemistry and beyond. The Journal of Chemical ...

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