{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/robust-quantum-dots-charge-autotuning-using","title":"Robust quantum dots charge autotuning using neural network uncertainty","arxiv_id":"2406.05175","date":"2024-06-07","proceeding":null,"authors":["Victor Yon","Bastien Galaup","Claude Rohrbacher","Joffrey Rivard","Clément Godfrin","Ruoyu Li","Stefan Kubicek","Kristiaan De Greve","Louis Gaudreau","Eva Dupont-Ferrier","Yann Beilliard","Roger G. Melko","Dominique Drouin"],"abstract":"This study presents a machine-learning-based procedure to automate the charge tuning of semiconductor spin qubits with minimal human intervention, addressing one of the significant challenges in scaling up quantum dot technologies. This method exploits artificial neural networks to identify noisy transition lines in stability diagrams, guiding a robust exploration strategy leveraging neural networks' uncertainty estimations. Tested across three distinct offline experimental datasets representing different single quantum dot technologies, the approach achieves over 99% tuning success rate in optimal cases, where more than 10% of the success is directly attributable to uncertainty exploitation. The challenging constraints of small training sets containing high diagram-to-diagram variability allowed us to evaluate the capabilities and limits of the proposed procedure.","url_abs":"https://arxiv.org/abs/2406.05175v3","url_pdf":"https://arxiv.org/pdf/2406.05175v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"robust-quantum-dots-charge-autotuning-using","repo_url":"https://github.com/3it-inpaqt/dot-calibration-v2","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"efficient-exploration","task_name":"Efficient Exploration"}],"methods":[],"datasets_introduced":[{"slug":"qdsd","name":"QDSD","full_name":"Quantum Dots Stability Diagrams"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}