A hybrid system that marries quantum computing with traditional high-performance computing is currently being developed in Austria.

The University of Texas at Austin is boosting its commitment to research and education in quantum science and engineering by establishing the Texas Quantum Institute.

The latest advances in quantum computing include investigating molecules, deploying giant supercomputers and building the quantum workforce with a new academic program. Researchers in Canada and the U.S. used a large language model to simplify quantum simulations that help scientists explore molecules.

Forschungszentrum Jülich is getting another quantum computer. The Jülich Supercomputing Centre (JSC) has acquired a 5-qubit quantum system from the German–Finnish manufacturer IQM Quantum Computers. The IQM Spark quantum computer is scheduled to go into operation in July 2024 as part of Jülich’s JUNIQ quantum computing infrastructure and will be connected to JSC’s classical supercomputers. This gives researchers the opportunity to investigate how calculations on classical supercomputers can be accelerated by quantum computers.

Using mathematical concepts first developed for highly specialized calculations in quantum physics and quantum information science, Zapata AI has implemented an innovative alternative to Monte Carlo simulations that dramatically reduces the time required to run these crucial calculations, while reducing their costs as well. In some scenarios, we’ve demonstrated that our method can provide a speed up of over 8,000x the time it takes to run a comparable Monte Carlo simulation using traditional techniques on the same computational hardware.

NVIDIA today announced that Japan’s new ABCI-Q supercomputer — designed to advance the nation’s quantum computing initiative — will be powered by NVIDIA platforms for accelerated and quantum computing.

Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on the use of data and algorithms to allow computers to learn without explicitly being programmed. While discussions surrounding AI algorithms, such as ChatGPT and other generative models, are taking place at all levels of society, the machine learning capabilities of quantum computers are still somewhat unexplored. Researchers around the world are currently working hard to answer the question of whether quantum computers will be able to better solve some of the problems presented by conventional machine learning. A study carried out by team of researchers from Freie Universität Berlin has now revealed remarkable insights that challenge previous assumptions about quantum machine learning. The team has discovered that neuronal quantum networks can not only learn but also memorize seemingly random data. The study, titled “Understanding Quantum Machine Learning Also Requires Rethinking Generalization” was published in the scientific journal Nature Communications and is available online

Riverlane, the global leader in quantum error correction technology, and Rigetti, a pioneer in full-stack quantum-classical computing, today announced their participation in a project led by the US Department of Energy’s Oak Ridge National Laboratory (ORNL) to explore the challenges of integrating a quantum computer with a large-scale, supercomputing centre.

Russia scientists told the country’s media service that the team has successfully developed a 20-qubit quantum computer and have plans to up the qubit number to “50 to 100” qubits. The news reveals the country’s ongoing advancements in this cutting-edge field.

The European High Performance Computing Joint Undertaking (EuroHPC JU) has launched a call for tender for the installation of LUMI-Q, a new EuroHPC quantum computer to be located in Czechia and integrated into the EuroHPC supercomputer KAROLINA.