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This article details a significant advancement in quantum computing infrastructure where an AI model automates routine calibration and measurement workflows for superconducting qubits. By leveraging software-controlled hardware, researchers can now focus on high-level experiment design rather than manual operations, marking a shift toward autonomous quantum labs.
Codex, powered by GPT-5.6 Sol, coordinates superconducting qubit tests
GPT-5.6 Sol, via Codex, now coordinates experiments with superconducting qubits at MIT, freeing researchers like Beatriz Yankelevich.
MIT graduate student Beatriz Yankelevich used GPT-5.6 Sol, harnessed to Codex, to automate routine measurements on superconducting qubit chips. This allowed her to shift focus from hands-on operation to higher-level research tasks like experiment design and data analysis. Superconducting qubits, cooled to near absolute zero inside dilution refrigerators, require software control, making them ideal for AI automation. Yankelevich found that GPT-5.6 Sol could often complete routine measurement workflows autonomously, saving significant time. GPT-5.6 Sol identified how long a superconducting qubit retained quantum information during recent experiments at MIT. Graduate student Beatriz Yankelevich connected the AI, harnessed to…
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