A collaboration between Rice University, Baylor College of Medicine and Texas Children’s Hospital’s Jan and Dan Duncan Neurological Research Institute (NRI) has produced a breakthrough in how to study and classify complex diseases. The new method, called the Causal Pivot, gives researchers a powerful way to detect hidden genetic drivers and subgroup patients by true biological causes of their illnesses using new computational tools developed in the research, paving the way for major advances in personalized genetic medicine. The research was recently published in the American Journal of Human Genetics. 

This September 14-19, at the 12th Heidelberg Laureate Forum (HLF), 200 young researchers in mathematics and computer science will spend a week of scientific exchange with the recipients of the disciplines’ most prestigious prizes: the Abel Prize, ACM A.M. Turing Award, ACM Prize in Computing, Fields Medal, the Nevanlinna Prize as well as its continuation, the IMU Abacus Medal. Below are a few highlights of the upcoming program of the HLF as well as a breakdown of how to cover this unique event.

Creating stable isolated spins is crucial for quantum-spin applications, like quantum bits or qubits, sensors, and single-atom catalysts. Currently, most approaches include engineering isolated spins on noble metal surfaces, whose abundant conduction electrons can also disturb their spin state. In a new study, researchers successfully demonstrated for the first time isolated spins on an insulating magnesium oxide film, laid over a ferromagnetic iron substrate, suggesting a new pathway towards realizing qubits using conventional thin-film techniques.

What do children’s building blocks and quantum computing have in common? The answer is modularity. It is difficult for scientists to build quantum computers monolithically – that is, as a single large unit. Quantum computing relies on the manipulation of millions of information units called qubits, but these qubits are difficult to assemble. The solution? Finding modular ways to construct quantum computers. Like plastic children’s bricks that lock together to create larger, more intricate structures, scientists can build smaller, higher quality modules and string them together to form a comprehensive system.

A review published in National Science Review highlights recent progress at the intersection of machine learning and quantum science, focusing on how AI techniques are revolutionizing the estimation and control of complex quantum systems.