PAC Studio Machine Learning: Human-in-the-Loop Analysis of TDPAC Spectra
PAC工作室机器学习:TDPAC光谱的人在回路分析
Thien Thanh Dang, Juliana Heiniger-Schell, Doru Constantin Lupascu
专题命中
工作流自动化
:workflow(abstract)
AI总结
研究TDPAC光谱分析这一病态反问题,通过PAC Studio ML软件集成多种功能,利用机器学习组件加速参数探索等,支持专家分析,提高工作流程速度与诊断透明度,展示不同工作流程及效果。
CommentsWithdrawn by the submitting author because the manuscript was posted before all co-authors had completed their review and approved its public release. A revised version may be submitted after all authors have reached agreement
Component-Level Inverse Design of Transmon Qubits Using Neural Networks
使用神经网络的跨导量子比特组件级逆设计
Olivia Seidel, Firas Abouzahr, Abhishek Chakraborty, Sadman Ahmed Shanto, Saikat Das, Daniel Baxter, Jonathan Asaadi, Nicola Pancotti, Haoyu Yang, Brucek Khailany, Sara Sussman, Enectali Figueroa-Feliciano, Eli M Levenson-Falk, Taylor L. Patti
Commentsv2: Clarified the methodology, validation, and runtime comparisons, and corrected minor presentation issues. Results and conclusions are unchanged
CommentsIncluded some information after review: In Introduction: included a paragraph with more references. In Figure 13 (a) and (b): refined the stress scale to machine precision. In Subsection 4.5: A remark is included addressing issues with cut elements. Subsection 5.4: included a sensitivity analysis of the penalty paramenter to the Hertzian contact problem
Comments34 pages, 7 figures. In the abstract of this updated version, we have removed the dot from the package name in the abstract; arxiv rendering generates a spurious link