IQM and NVIDIA Automate 20-Qubit Chip Calibration via NVIDIA Ising Vision-Language Models and QCalEval Benchmark

European superconducting quantum hardware provider IQM Quantum Computers and NVIDIA have demonstrated the agentic AI calibration of a 20-qubit IQM superconducting quantum processor using specialized NVIDIA Ising Calibration vision-language models (VLMs).

Integrated directly into IQM’s proprietary hardware control and calibration software, the agentic VLM stack analyzed raw experimental tuning charts, autonomously determined optimal parameter adjustments, and executed dynamic bring-up routines across all 20 physical qubits.

The workflow automatically identified out-of-bounds qubit parameter settings, dynamically adjusted measurement parameter ranges, and generated plain-English diagnostic logs explaining each calibration decision for human auditability.

Each chart presents six diagnostic questions evaluating a model’s ability to identify parameter anomalies and select corrective actions.

On the public QCalEval leaderboard, specialized NVIDIA Ising Calibration models scored up to 19 points higher than general-purpose vision-language models.