Collaborative Calculation and Data Science

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![](https://cocalc.com/share/raw/fb8f75e82c384704cdee9885a0e654ff99a6f85d/Images/NVIDIA%20CUDA-Q/Screenshot%202025-06-11%20at%203.58.40%E2%80%AFPM.png)

This week, NVIDIA highlighted CoCalc as a key platform for teaching with its CUDA-Q academic materials. In their technical blog post, NVIDIA mentions how CoCalc can provide a seamless learning environment for the next wave of quantum computing specialists.

This might also be a good time to add that we were officially accepted as an NVIDIA Inception Program Member a while back! 

Our Chief Sales Officer, Blaec Bejarano, has had the pleasure of meeting Monica Van Dieren, a Senior Technical Marketing Engineer at NVIDIA, at the Joint Mathematics Meeting in Seattle this past January. Their discussions continued at the NVIDIA GTC conference in San Jose in March, solidifying our shared vision for accessible and powerful quantum computing education.

NVIDIA's CUDA-Q Academic program is a comprehensive suite of Jupyter notebooks designed to bridge the gap between theoretical quantum mechanics and practical application. These resources are now readily available via CoCalc, allowing students and instructors to dive into complex topics like quantum machine learning and variational algorithms without the hassle of a complex setup.

![](https://cocalc.com/share/raw/3238e4adaf86c967df5f80188b645a570786833a/Images/NVIDIA%20CUDA-Q/Screenshot%202025-06-11%20at%204.02.29%E2%80%AFPM.png)

The synergy between CoCalc's collaborative platform and NVIDIA's cutting-edge educational content creates an unparalleled learning experience. Students can work through CUDA-Q modules, leveraging CoCalc's powerful computational resources and real-time collaboration features. This integration is particularly highlighted in NVIDIA's post, which notes the ease of getting started on platforms like CoCalc.

![](https://cocalc.com/share/raw/a1392785b6fba7e16f11aa0af5389ef087d047b0/Images/NVIDIA%20CUDA-Q/Screenshot%202025-06-11%20at%203.07.59%E2%80%AFPM.png)

For those eager to explore these resources, the CUDA-Q Academic GitHub repository is the perfect starting point: https://github.com/NVIDIA/cuda-q-academic/tree/main?tab=readme-ov-file

We are thrilled to be at the forefront of education, providing the tools necessary to train the quantum workforce of the future. The journey with NVIDIA is just beginning, and we look forward to empowering more learners around the globe.

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