Researchers have fully mapped how noise spreads through quantum computers over time to show that glitches link together across different moments, creating a form of 'memory' that undermines calculations.

 

An interdisciplinary team of University of Tennessee, Knoxville researchers recently published in Biophysical Journal on their development of a new statistical method that improves analysis in single-molecule fluorescence experiments, which are used to study important protein complexes in cells.

For the first time worldwide, we have achieved remote, real-time control of fusion plasma using a digital twin running on a supercomputer located about 1,000 km away (round-trip network path ~2,000 km).

In magnetic confinement fusion power, sustaining and precisely controlling plasma at temperatures exceeding 100 million ℃ over long durations is essential. Yet “predicting-while-controlling” has been challenging due to model accuracy limits, computation speed, and unresolved physics. Our team has developed a system that applies data assimilation, continuously updating the predictive model with real-time measurements to improve accuracy and using accelerated parallel prediction to determine optimal unrehearsed control actions.

A research team from Kyoto University, the National Institute for Fusion Science (NIFS), the National Institutes for Quantum Science and Technology (QST), and the Institute of Statistical Mathematics (ISM), has connected the Large Helical Device (LHD) in Toki, Gifu, Japan to the new “Plasma Simulator” supercomputer in Rokkasho, Aomori, jointly procured by NIFS and QST, via the high-quality, high-bandwidth academic network SINET6. By exclusively using more than 20,000 Central Processing Unit (CPU) cores and minimizing communication latency, the team has realized real-time predictive control of LHD from a remote supercomputer. This approach — linking a large experimental facility and a large computing system over a ~2,000 km network loop — can serve as a foundation for real-time control beyond fusion.

Researchers at McMaster University have developed a new database that brings together more than 100 years of historical epidemiological data from across Canada, which will help to predict future patterns of infectious disease.

A research team led by Prof. Tao Jiang and Prof. Jing Li from State Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, in collaboration with Prof. Shi-Shun Zhao from College of Mathematics, Jilin University and Prof. Jianwei Wang from NHC Key Laboratory of Systems Biology of Pathogens and Christophe Merieux Laboratory, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, developed a viral risk prediction framework named GIVAL based on the pre-trained viral protein language model vBERT. This study proposes a general intelligent prediction method for assessing adaptation risks of unknown viruses.

A team from the Institute of Industrial Science, The University of Tokyo has developed MatAgent, an AI system powered by a large language model. By reasoning in natural language, using memory, and learning from feedback, MatAgent explores new inorganic materials with a human-like process while explaining each step it takes.