| name | vibe-calibration |
| description | Vibe Calibration methodology for autonomous quantum processor bring-up using LLM skill orchestration. Distills expert tacit knowledge into reusable calibration skills for superconducting quantum processors. Use when designing autonomous calibration systems for quantum hardware, LLM-orchestrated experimental control, or skill-based bring-up workflows for complex scientific instruments. Triggers: quantum calibration, autonomous bring-up, LLM agent experiment, superconducting qubit, skill orchestration, quantum processor tuning, vibecoding calibration. |
Vibe Calibration
Methodology from arXiv:2606.22376v1 (Jun 21, 2026) — Autonomous Bring-up of a 112-Qubit Superconducting Quantum Processor by a Skill-Orchestrating Language Agent.
Core Problem
Scaling superconducting quantum processors (>100 qubits) faces a calibration bottleneck: conventional scripts are brittle to anomalous signals, and expert judgment cannot keep pace with system scale.
Key Innovation
Vibe Calibration uses LLM agents to orchestrate reusable calibration skills, distilling expert tacit knowledge into structured, composable workflows. The system achieved autonomous bring-up of a 112-qubit processor.
Architecture
1. Skill Library
- Pre-defined calibration primitives (e.g., frequency find, Rabi calibration, Ramsey tuning)
- Each skill encapsulates: procedure, success criteria, failure recovery
- Skills are composable — higher-level skills chain lower-level ones
2. LLM Orchestrator
- LLM agent selects and sequences skills based on system state
- Interprets measurement data qualitatively (beyond threshold-based scripts)
- Adapts strategy when anomalies are detected
- Maintains calibration state across the full bring-up process
3. Closed-Loop Execution
Measure → LLM interprets → Select skill → Execute → Evaluate → Iterate
4. Knowledge Distillation
- Expert tacit knowledge → Structured skill descriptions
- Natural language skill specifications → Executable procedures
- Failure modes captured as skill-specific recovery strategies
Key Principles
- Skill-based modularity: Decompose calibration into reusable, composable skills
- LLM as interpreter: Use LLMs to interpret qualitative measurement patterns
- Tacit knowledge capture: Encode expert intuition into structured skill descriptions
- Adaptive sequencing: Let the orchestrator decide skill order dynamically
Application Domains
- Superconducting quantum processor calibration
- Any complex experimental system requiring expert tuning
- LLM-orchestrated scientific automation
- Skill-based autonomous experimental workflows
Activation
Keywords: quantum calibration, autonomous bring-up, LLM agent experiment, superconducting qubit, skill orchestration, vibecoding, quantum processor tuning, autonomous experiment
