Quantum algorithms for improving surface coatings
Business Announcement
Updates every hour. Last Updated: 5-May-2026 09:16 ET (5-May-2026 13:16 GMT/UTC)
In addition to immediate health risks, UV radiation also poses indirect hazards: it corrodes surface coatings on exposed objects (e.g., on aircraft and bridges) and attacks the coated materials. The underlying molecular processes (polymer degradation) are extremely complex. Therefore, a consortium coordinated by Fraunhofer IAF is working within the framework of the BMFTR-funded QPolyDeg project to develop novel quantum algorithms for simulating polymer degradation. Quantum chemical calculations are intended to enable more durable coatings for industrial applications.
The Alliance for Clinical Trials in Oncology is now enrolling patients in the ASPIRE trial (A032302)—a large-scale, phase III clinical study investigating whether adding chemotherapy to current standard treatments extends survival for men with advanced prostate cancer.
A study focusing on fundamental aspects of quantum physics led by Cal Poly Physics Department Lecturer Ian Powell analyzed how a changing magnetic field can make matter behave in unusual ways. Working in collaboration with student researcher Louis Buchalter, an article coauthor, Powell published the journal article “Flux-Switching Floquet Engineering,” which demonstrates that changing magnetic fields over time in time can create quantum states that do not exist in any stationary material. By engineering new quantum behaviors by timing the field, physicists can potentially create technologies that are very stable and hard to disrupt by “noise” or imperfections that can interfere with quantum technology functionality and avoid system errors.
How does one plan a space mission that involves visiting multiple celestial bodies which are constantly moving? Researchers at Bielefeld University have, for the first time, developed a precise mathematical approach to this problem. The publication in a leading international journal demonstrates that decision-support methods at the interface between economics and mathematics can advance space travel and transport planning – with implications extending far beyond space missions.
Penn Engineers have developed a new way to use AI to solve inverse partial differential equations (PDEs), a particularly challenging class of mathematical problems with broad implications for understanding the natural world. The advance, which the researchers call “Mollifier Layers,” could benefit fields as varied as genetics and weather forecasting, because inverse PDEs help scientists work backward from observable patterns to infer the hidden dynamics that produced them.