Robot Tuner for Quantum Processors ⚡ экспресс
Calibrating superconducting quantum processors becomes a critical bottleneck when scaling up: traditional scripts are unstable in the face of anomalous signals, and manual tuning by experts is limited by time and cognitive load. To overcome this, the Vibe Calibration system was developed — a fully autonomous calibrator driven by large language model agents. Expert knowledge is formalized into skills — decision trees that combine parameterized measurement commands, quantitative acceptance criteria, and audit logs for self-diagnosis. Knowledge is extracted in three stages with human input and used to fine-tune the language model on validated trajectories. On a 112 frequency-tunable transmon processor, the system autonomously calibrated 108 qubits in 4.7 hours, achieving a 4–5× speedup over full manual calibration. Cross-validation on a subset of 16 qubits showed agreement with an expert in 14 cases. The key achievement is transferable workflows: the core decision-making logic and task orchestration generalize to new devices with minimal adaptation of low-level scripts.
Tuning a quantum processor is monotonous work: you need to calibrate each qubit — a quantum cell — much like a watchmaker adjusting dozens of tiny gears. Humans get tired and make mistakes.
Now this "apprentice" carries out parameter measurements, makes decisions, and keeps records on its own. At its core is a language model that typically generates text, but here it controls real hardware. The system doesn't just repeat memorized steps; it understands the essence, so it handles unexpected situations and easily transfers skills to other chips. Reducing chaos in the system yields stable results.
🎯 Vibe Calibration is powered by a large language model — the same technology behind ChatGPT, but adapted for quantum physics.