Self-Specializing Vision-Language Transmon Chip Calibration in a Physics-Grounded Environment
arXiv:2607.03193v1 Announce Type: cross Abstract: Calibrating a superconducting transmon chip is a sequential decision problem under noise, drift, and a finite budget: an expert must choose experiments, read ambiguous plots, judge fit quality, and revise stale beliefs as the chip drifts. We study whether a vision-language agent can close this loop and specialize itself to one physical device without weight updates, via three co-designed artifacts. The first is a physics-grounded simulation environment for transmon chips: calibration observables derive from circuit-quantized parameters via scqu
This paper leverages recent advancements in vision-language models and applies them to the complex, iterative process of quantum chip calibration, combining AI's emergent capabilities with a crucial bottleneck in quantum computing development.
Automating and specializing the calibration of complex quantum hardware using AI agents could significantly accelerate the development and deployment of quantum computers, reducing human expert dependency and operational costs.
The labor-intensive, expert-dependent process of quantum chip calibration could become more autonomous and efficient, potentially leading to faster iteration cycles and improved quantum device performance.
- · Quantum computing companies
- · AI software developers
- · Hardware manufacturers
- · Quantum researchers
- · Manual calibration experts (short-term)
- · Companies without AI integration strategies
More efficient and reliable quantum chip production becomes possible, accelerating quantum research and development.
Reduced barriers to entry for operating quantum hardware could broaden the adoption of quantum computing across industries.
The principles developed here could be generalized to other complex hardware calibration problems, across various advanced technology sectors.
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Read at arXiv cs.AI