In a recent study, a research team from Chiba University in Japan developed a novel data assimilation technique designed for quantum annealing machines. In the study, the team formulated the four-dimensional variational data assimilation (4DVAR) cost function, into a quadratic unconstrained binary optimization (QUBO) problem, which quantum annealers can solve. This new quantum annealing approach accelerates data assimilation, thereby addressing a main computational bottleneck for numerical weather predictions.

Scientists from the Japan Advanced Institute of Science and Technology (JAIST) have recently proposed a symbolic approach for automatic model-checking of quantum circuits, aiming to bridge the gap between model-checking quantum programs and quantum circuits. This novel method, developed using basic matrix operations in quantum mechanics laws and the Maude programming language, marks a crucial first step towards a universal framework for quantum circuit verification and specification.

Multiverse Computing, a developer of AI and quantum software solutions, announced yesterday that it had won 800,000 hours of computing time on a supercomputer to build a large language model (LLM) in the Large AI Grand Challenge organized by the AI-BOOST project. This project, funded by the European Commission, aims to build and train an LLM from scratch using quantum and quantum-inspired technology.

Quantum Circuits recently announced the launch of an integrated quantum software platform to pave the way for its forthcoming quantum hardware systems and accelerates the path to commercial-ready, fault-tolerant quantum computing. The company said that software enables users to manage qubit error detection in their algorithms in real time, thus enhancing the algorithms' performance and scalability.