Shared tool developed for quantum and supercomputer systems
Reports and Proceedings
Updates every hour. Last Updated: 7-Nov-2025 09:11 ET (7-Nov-2025 14:11 GMT/UTC)
Quantum computers are a key emerging technology, particularly for specific kinds of problems that require enormous computing power. However, integrating quantum systems into existing supercomputers poses a challenge. Researchers at the Technical University of Munich (TUM) have developed a tool that combines quantum and supercomputers and enables them to interact seamlessly. This approach has been demonstrated experimentally in collaboration with a team at the Leibniz Supercomputing Centre (LRZ).
A new study by an SUTD researcher and his collaborators introduces a pooled mining attack that overturns a long-standing assumption about Bitcoin’s security economics.
A research team has combined drone-based imaging with advanced data analytics to track plant height across hundreds of cotton varieties, revealing both known and previously unidentified genes controlling stem growth.
The Hertz Foundation, a nonprofit dedicated to advancing American scientific and technological leadership, today announced that the application for the 2026 Hertz Fellowship is now open. The Hertz Fellowship is one of the most competitive and coveted awards for doctoral students in applied sciences, engineering and mathematics. Hertz Fellows receive up to five years of funding, giving them freedom from the traditional constraints of graduate training and the independence needed to pursue groundbreaking research. They also gain lifelong professional support, including mentoring, events and networking opportunities.
When genetic testing reveals a rare DNA mutation, doctors and patients are frequently left in the dark about what it actually means. Now, researchers at the Icahn School of Medicine at Mount Sinai have developed a powerful new way to determine whether a patient with a mutation is likely to actually develop disease, a concept known in genetics as penetrance. The team set out to solve this problem using artificial intelligence (AI) and routine lab tests like cholesterol, blood counts, and kidney function. Details of the findings were reported in the August 28 online issue of Science. Their new method combines machine learning with electronic health records to offer a more accurate, data-driven view of genetic risk.