**TechFlame News** — Nvidia has released an open-source AI model, the **NVIDIA Ising Calibration 1.5**, designed to automatically analyze diagnostic data from quantum processors (QPUs) and autonomously determine device tuning strategies, thereby automating the calibration workflow of quantum computers. According to Nvidia, Ising Calibration 1.5 is a vision-language model (VLM) specifically built for quantum computing calibration scenarios. It can interpret experimental data from quantum chips, perform "zero-shot" analysis even in the absence of historical cases, and leverage relevant experimental samples for in-context learning (ICL) to help quantum devices continuously optimize their operational state. In the QCalEval quantum calibration benchmark, Ising Calibration 1.5 outperforms open-source models of similar scale by an average of approximately 10% in zero-shot reasoning. When utilizing relevant experimental cases for in-context learning, its performance improves by about 86.5% compared to the previous generation model, surpassing multiple open-source models and approaching the level of top-tier closed-source large models. The model features 31 billion parameters and supports Nvidia's Grace Blackwell, Vera Rubin, and other data center GPUs. Additionally, an NVFP4 quantized version has been released, which can be deployed on a single consumer-grade GPU or the NVIDIA DGX Spark, significantly lowering the barrier to entry for quantum laboratories. Nvidia states that Ising Calibration 1.5's training data is derived from a variety of qubit architectures, including superconducting qubits, quantum dots, ions, neutral atoms, and helium surface electrons, enabling calibration capabilities for different types of quantum computing devices. Industry experts believe that automated calibration is one of the key bottlenecks for the large-scale development of quantum computing. Nvidia’s introduction of this AI-driven quantum calibration tool signals that AI models are beginning to extend from traditional computing domains into the hardware control layer of quantum systems, potentially becoming a critical component of the future quantum computing industrial infrastructure.