Explore the 22-qubit demo and see #CUDAQ in action: https://nvidia.github.io/cuda-quantum/latest/applications/python/skqd.html?ncid=so-twit-844277

🔗 Dive into the benchmarks and code samples here: https://developer.nvidia.com/blog/advanced-large-scale-quantum-simulation-techniques-in-cuquantum-sdk-v25-11/?ncid=so-twit-654891&linkId=100000398133283

Quantum developers can now achieve up to 100x speedups for validating large scale utility circuits, and 1000x speedups for simulating #QuantumErrorCorrection codes. ✨

Professor Hao Zhang says DGX B200 “enables us to prototype and experiment much faster than using previous-generation hardware,” with performance among the best in the world.

We look forward to seeing the results of their research and projects. 🙌

Read the full technical deep dive: https://developer.nvidia.com/blog/real-time-decoding-algorithmic-gpu-decoders-and-ai-inference-enhancements-in-nvidia-cuda-q-qec/?ncid=so-twit-566140&linkId=100000397936187

#QPU builders and researchers can now run AI and GPU-accelerated decoders for both real and simulated QPUs, scaling #quantum hardware into useful quantum-GPU supercomputers.

Learn how to leverage this framework in our latest tech blog. 👉 https://developer.nvidia.com/blog/using-ai-physics-for-technology-computer-aided-design-simulations/?ncid=so-twit-105750

https://www.nature.com/articles/s41467-025-65836-3?linkId=100000396517062&error=cookies_not_supported&code=87a19b0e-2c20-4f2b-a697-ea94f79341ab

Dive into our Nature Communications review to see how state-of-the-art AI is accelerating everything from qubit design and control to real-time error correction, paving the way for useful fault-tolerant QC.

🌌 We provide detailed visibility into application algorithms, so you can fine tune performance where it matters most, with NVIDIA Nsight Systems, a system-wide performance analysis tool.

➡️ https://developer.nvidia.com/nsight-systems?ncid=so-twit-907069