Automating physical experiments via Large Language Models: an attempt on superconducting quantum processors

ORAL

Abstract

Large Language Models has been recorganized a substantial advancement in the AI domain, driving new applications in content creation and code development fields. In this study, we introduce our attempt to employ Large Language Models in establishing an autonomous environment for the calibration processes of superconducting qubits, based on retrival argumented generation. We compare the performance of different large language models on analysing the experimental data and making decisions on the subsequent steps in the experiment.

Presenters

  • Zijian Zhang

    • University of Toronto

Authors

  • Zijian Zhang

    • University of Toronto
  • Shuxiang Cao

    • University of Oxford
  • Mohammed Alghadeer

    • University of California, Berkeley
    • University of Oxford
  • Simone D Fasciati

    • University of Oxford
  • Michele Piscitelli

    • University of Oxford
  • Mustafa S Bakr

    • University of Oxford
  • Peter J Leek

    • University of Oxford
  • Alán Aspuru-Guzik

    • University of Toronto