vlm-transmon-calibration

name	vlm-transmon-calibration
description	Self-specializing Vision-Language Model agent for physics-grounded transmon chip calibration. Uses zero-weight-update online adaptation via human-readable device notes, gradient-free strategy refinement with paired-snapshot accept gate, and physics-grounded simulation with realistic drift/wall-time/leakage. Activation: transmon calibration, quantum chip tuning, VLM calibration agent, gradient-free online adaptation, superconducting qubit calibration, 量子芯片校准
metadata	{"arxiv_id":"2607.03193","published":"2607-07-03","authors":"VLM transmon calibration authors","tags":["quantum","calibration","vlm","transmon","gradient-free","online-adaptation"]}

VLM Transmon Chip Calibration

Core Methodology

Problem: Superconducting transmon chip calibration is a sequential decision problem under noise, drift, and finite budget. Experts must choose experiments, read plots, judge fits, and revise beliefs as chips drift.

Solution: VLM agent closes the calibration loop end-to-end via 3 co-designed artifacts:

1. Physics-grounded simulation environment

   • Calibration observables from circuit-quantized parameters (scqubits)
   • Realistic flux-line distortion, wall-time-scaled drift, gate leakage
   • Each tool call advances modeled clock — drift accrues by wall time, not call count

2. Vision-Language Agent loop

   • Calls tools, reads plots, maintains structured notebook
   • Submits parameters without hidden truth access
   • Scored against hidden parameters and measured gate fidelities

3. Gradient-free online adaptation

   • Reflector reads truth-free anomaly signatures from past attempts
   • Grows small human-readable device note appended to prompt
   • Paired-snapshot accept gate isolates strategy improvement from drift

Results: On hard-tier chip, 6 iterations raised worst-case CZ fidelity from 0.678→0.787. Single accepted note raised CZ from 0.678→0.913 on paired snapshot.

Key Design Patterns

Pattern 1: Physics-Grounded Simulation

When simulating quantum hardware for agent training:

• Derive observables from actual circuit parameters (scqubits)
• Include realistic noise: flux-line distortion, wall-time drift, gate leakage
• Advance simulation clock per action — drift is time-based, not step-based

Pattern 2: Gradient-Free Online Adaptation

When adapting an agent without weight updates:

• Maintain structured notebook of past attempts
• Extract anomaly signatures (patterns of failure without truth access)
• Append concise device notes to prompt
• Use paired-snapshot accept gate: compare strategy on frozen snapshot before/after

Pattern 3: Planted-Fault Diagnosis

When testing calibration agent capabilities:

• Plant known hardware faults in simulation
• Verify agent diagnoses faults truth-free
• Measure: does the device note causally improve fidelity?

Activation Keywords

• transmon chip calibration
• quantum chip tuning agent
• VLM calibration
• gradient-free online adaptation
• superconducting qubit calibration
• physics-grounded quantum simulation
• 量子芯片校准
• 超导量子比特标定

Related Skills

• hardware-safety-gated-llm-quantum-control — LLM-written quantum control
• rl-ion-shuttling — RL for trapped-ion control
• model-based-rl-quantum-control — RL for robust quantum control
• vibe-calibration-autonomous-quantum — autonomous quantum calibration