Onnes: A Physics-Grounded Multi-Agent LLM Simulator for Cryogenic Fault Diagnosis in Quantum Computing Infrastructure
arXiv:2607.05805v1 Announce Type: new Abstract: Dilution refrigerators are the enabling infrastructure of superconducting quantum computers, yet their fault diagnosis is still dominated by threshold alarms that report that something is wrong, not what. We present Onnes, a physics-grounded digital-twin simulator of a dilution refrigerator (a forward physics model with a learned real-fridge noise fingerprint) that drives a live multi-agent LLM operations layer, and use it for a controlled head-to-head between a zero-shot LLM agent panel and a supervised ML classifier on cryogenic fault diagnosis
The increasing complexity and scale of quantum computing infrastructure necessitate more sophisticated and autonomous diagnostic tools to maintain operational uptime and advance the field.
This development represents a significant step towards autonomous operations and fault diagnosis in highly complex, sensitive, and critical infrastructure like quantum computers, paving the way for broader AI-driven maintenance across advanced tech sectors.
The reliance on human experts for diagnosing subtle and complex faults in cryogenic systems can be significantly reduced, leading to faster problem resolution and enhanced quantum computer reliability.
- · Quantum Computing sector
- · AI agents developers
- · Cryogenic equipment manufacturers
- · High-tech infrastructure operators
- · Traditional fault diagnosis services
- · Operators reliant on manual troubleshooting
Improved uptime and efficiency for quantum computers through AI-driven fault diagnosis.
Accelerated development and application of quantum computing due to more reliable infrastructure.
Proliferation of physics-grounded multi-agent LLM simulators across other complex scientific and industrial systems.
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Read at arXiv cs.AI