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NishMath - #computation
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@blogs.nvidia.com
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Recent advancements in quantum computing include the launch of new supercomputers and the development of open-source frameworks. NVIDIA and AIST have collaborated to launch ABCI-Q, a supercomputing system designed for hybrid quantum-AI research. This system, powered by NVIDIA H100 GPUs and utilizing NVIDIA’s Quantum-2 InfiniBand platform, is hosted at the Global Research and Development Center for Business by Quantum-AI Technology (G-QuAT). ABCI-Q supports hybrid workloads by integrating GPU-based simulation with physical quantum processors from Fujitsu, QuEra, and OptQC, aiming to advance quantum error correction and algorithm development. It serves as a testbed for quantum-GPU workflows across various hardware modalities.
Quantum Machines has introduced QUAlibrate, an open-source calibration framework designed to significantly reduce the time required for quantum computer calibration. Calibration, a major hurdle in quantum system performance and scalability, can now be reduced from hours to minutes. QUAlibrate enables the creation, execution, and sharing of modular calibration protocols, allowing researchers to calibrate multi-qubit superconducting systems rapidly. At the Israeli Quantum Computing Center, full multi-qubit calibration was achieved in just 140 seconds using QUAlibrate. The framework is built on the QUA programming language and uses the Quantum Abstract Machine (QUAM) to model quantum hardware, featuring a graph-based calibration approach. These advancements are supported by strategic collaborations and investments in quantum technologies. SilQ Connect, a startup focusing on distributed quantum computing, has secured pre-seed funding to advance modular quantum interconnects. This funding from QV Studio, Quantacet, and Quantonation will support the development of microwave-optical quantum interconnects for scalable quantum systems. Additionally, Taiwan's National Center for High-Performance Computing is deploying a new NVIDIA-powered AI supercomputer to support research in climate science, quantum research, and the development of large language models. This initiative aims to foster cross-domain collaboration and global AI leadership.
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@phys.org
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Recent research has spotlighted the diverse applications of mathematical and computational methods across multiple critical fields. One notable study, published in ACM Transactions on the Web, details the use of advanced mathematical techniques and software to investigate the collapse of the TerraUSD stablecoin and its associated currency, LUNA. Led by Dr. Richard Clegg at Queen Mary University of London, the research team employed temporal multilayer graph analysis to uncover suspicious trading patterns indicative of a coordinated attack, which led to the loss of $3.5 billion. The study highlights the power of mathematical tools in unraveling complex financial events.
Scientists have also made significant strides in fluid dynamics through the development of AI-powered simulation models. Researchers at Osaka Metropolitan University have created a machine learning model that dramatically reduces computation time for fluid simulations while maintaining accuracy. This innovation, which utilizes graph neural networks, has potential applications in offshore power generation, ship design, and real-time ocean monitoring, offering a scalable solution that balances accuracy with efficiency. The new model cuts simulation time from 45 minutes to just three minutes. The 23rd International Conference of Numerical Analysis and Applied Mathematics (ICNAAM 2025) also focuses on the integration of mathematical and computational methods across science and engineering. A session within the conference aims to unite researchers and practitioners in discussing novel ideas, methodologies, and applications that bridge the gap between mathematics and its practical implementations. The session welcomes contributions focusing on analytical and numerical techniques, algorithm development, and computational modeling, particularly those providing new insights into solving complex systems.
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