Building a useful quantum computer in practice is incredibly challenging. Significant improvements are needed in the scale, fidelity, speed, reliability, and…
Building a useful quantum computer in practice is incredibly challenging. Significant improvements are needed in the scale, fidelity, speed, reliability, and programmability of quantum computers to fully realize their benefits. Powerful tools are needed to help with the many complex physics and engineering challenges that stand in the way of useful quantum computing.

NVIDIA CUDA-Q (formally NVIDIA CUDA Quantum) is an open-source programming model for building quantum accelerated supercomputing applications that take full…
Lattice structures are naturally and artificially made designs that are important in many scientific fields, such as materials science, aerospace engineering,…
CUDA Graphs can provide a significant performance increase, as the driver is able to optimize execution using the complete description of tasks and…
In its most fundamental form, AI is the capability of a computer program or a machine to think, learn, and take action without being explicitly encoded with…
In our previous exploration of graph analytics, we uncovered the transformative power of GPU-CPU fusion using NVIDIA cuGraph. Building upon those insights, we…
In the fast-evolving landscape of generative AI, the demand for accelerated inference speed remains a pressing concern. With the exponential growth in model…
Telecommunications companies (telcos) are leveraging generative AI to increase employee productivity by automating processes, improving customer experiences,…