INQUBATOR, Fraunhofer's quantum computing consulting and testing center, offers businesses an practical and cost-effective access to quantum computing. Starting immediately, companies of all sizes can apply with their use cases to develop customized solutions in collaboration with Fraunhofer partners and test the potential of quantum algorithms. The application deadline is August 31, 2026.

RNA design is central to next-generation therapeutics, yet identifying sequences that reliably fold into desired structures remains a major computational challenge, often constrained by high cost and time. A new study from Keio University explores the use of factorization machine with quadratic-optimization annealing (FMQA) for RNA inverse folding, while also examining how different encoding strategies may influence artificial intelligence (AI)-driven design performance, revealing an underexplored dimension of biomolecular engineering.

Weyl topological superconductivity is a rare state promising for quantum computing, but it usually requires complex band properties like strong spin-orbit coupling. Researchers have now shown that transitioning from a 2D square to a 3D cubic lattice, simple repulsive interactions between electrons create geometric frustration. This naturally forms a unique octupolar Weyl superconducting state. By removing the need for spin-orbit coupling, this opens a simpler, universal path for discovering new quantum materials and realizing unconventional 3D superconductivity

A research team led by Prof. Zhen Gao at Southern University of Science and Technology has achieved the first experimental observation of the three-dimensional (3D) photonic quantum Hall effect of Fermi arcs and its hallmark feature, the one-sided chiral hinge states, in a 3D inhomogeneous magnetic Weyl photonic crystal.

Genomic surveillance — the process of monitoring and sequencing pathogens — is one of the most important tools for detecting emerging viral threats. But global surveillance systems remain costly, unevenly distributed and often are too slow to identify dangerous variants before they spread internationally,  amplifying future threats of disease outbreaks.

A recently published research paper in Nature Communications, co-authored by Dr. Patricia Ning, assistant statistics professor, and Jifan Li, a doctoral candidate in the Department of Statistics, along with collaborators from multiple international institutions, introduces a new framework to rectify these issues while using fewer resources, making genomic surveillance rapid and cost-effective in preparation for new strains of COVID-19.