This one's worth opening because it puts an AI agent on an actual night shift in a physics lab, and the white paper is honest about where it stumbles.
MIT's EQuS lab hooked GPT-5.6 Sol to an uncharacterized superconducting quantum chip through a lightweight Jupyter MCP interface. The model ran 200 measurements over 12 hours and fully calibrated all six readout resonators. On four fixed-frequency qubits with clean signals, it handled 40 target measurements with only four human interventions, and delivered full T1/T2 data for the first qubit.
Don't read this as "AI replaces experimental physicists." The white paper says plainly: the agent is slower than human experts, lacks physical intuition, and on tunable qubits with poor SNR it accepted bad measurements as good. Figure 6 shows a classic failure: the model kept adjusting scan frequencies but never noticed the fit was off, until a human stepped in and told it to widen the range.
The useful bit isn't "AI is amazing" — it's the clear boundary line. Fixed-frequency qubits have sharp resonance peaks and mathematically verifiable acceptance criteria, so the agent works as a reliable night-shift operator. Tunable qubits produce messy signals that break the automated checks, and the system goes off the rails. That distinction is more actionable than any benchmark score.
I'd discount this a bit: it's a single white paper, no peer review, no open-source code, and no third-party confirmation that EQuS uses this routinely. OpenAI's own case study page admits "experienced researchers may still find optimal calibration parameters faster than current AI models." This looks more like an engineering validation than a production workflow.
But the direction is solid. The bottleneck in physics labs isn't compute — it's human attention. Letting an agent babysit repetitive measurements overnight and on weekends, while researchers do async spot-checks, keeps expensive fridges from sitting idle. That's a much more grounded entry point for AI in science than chasing autonomous discovery.