NVIDIA releases quantum computing AI calibration model, promoting the fusion of AI and quantum

NVIDIA released the open-source AI model NVIDIA Ising Calibration 1.5, designed for the automatic analysis of quantum processor (QPU) diagnostic data, and to autonomously determine device calibration schemes, achieving automation of the quantum computer calibration process. NVIDIA stated that Ising Calibration 1.5 is a visual language model (VLM) specifically designed for quantum computing calibration scenarios, capable of understanding experimental data from quantum chips and performing "zero-shot" analysis in the absence of historical cases, while also enabling context learning (ICL) with relevant experimental samples to help continuously optimize the operational state of quantum devices.

In the QCalEval quantum calibration benchmark test, Ising Calibration 1.5 averaged about 10% ahead of similarly sized open-source models in zero-shot inference capability, and when using relevant experimental cases for context learning, it showed an approximately 86.5% performance improvement over the previous generation model, surpassing multiple open-source models and approaching the level of top closed-source large models. The model has 31 billion parameters and supports operation on NVIDIA Grace Blackwell and Vera Rubin data center GPUs. It also launched an NVFP4 quantized version, which can be deployed on a single consumer-grade GPU or NVIDIA DGX Spark, significantly lowering the usage threshold for quantum laboratories.

NVIDIA claims that the training data for Ising Calibration 1.5 comes from various qubit architectures, including superconducting qubits, quantum dots, ions, neutral atoms, and helium surface electrons, providing calibration capabilities for different types of quantum computing devices. Industry experts believe that automated calibration is one of the key bottlenecks in the scalable development of quantum computing. NVIDIA's launch of this AI-driven quantum calibration tool signifies that AI models are beginning to extend from traditional computing domains into the quantum hardware control layer, potentially becoming an important component of the future quantum computing industry infrastructure.