[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126832-en":3,"doc-seo-126832-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126832,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning","Device variability slows the operation of semiconductor quantum circuits because each device must be tuned to specific working conditions using a distinct protocol. A machine-learning framework automates tuning of three gate-defined devices—a 4-gate Si FinFET, a 5-gate GeSi nanowire, and a 7-gate Ge/SiGe heterostructure double quantum dot—starting from scratch with a shared algorithm. Tuning completes in 30, 10, and 92 minutes, while also mapping the parameter-space landscape and identifying double quantum dot regimes. The results demonstrate scalable, architecture-spanning quantum-device tuning enabled by machine learning.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nCross‑architecture tuning of silicon and SiGe‑based quantum devices using machine learning  \nB. Severin1, D. T. Lennon1, L. C. Camenzind2, F. Vigneau1, F. Fedele1, D. Jirovec 3, A. Ballabio4, D. Chrastina4, G. Isella4, M. de Kruijf2, M. J. Carballido2, S. Svab2, A. V. Kuhlmann2, S. Geyer2, F. N. M. Froning2, H. Moon1, M. A. Osborne 5, D. Sejdinovic6, G. Katsaros 3, D. M. Zumbühl2,  \nG. A. D. Briggs1 & N. Ares 5*  \nThe potential of Si and SiGe‑based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions and each device realisation requires a different tuning protocol. We demonstrate that it is possible to automate the tuning of a 4‑gate Si FinFET, a 5‑gate GeSi nanowire and a 7‑gate Ge/SiGe heterostructure double quantum dot device from scratch with the same algorithm. We achieve tuning times of 30, 10, and 92 min, respectively. The algorithm also provides insight into the parameter space landscape for each of these devices, allowing for the characterization of the regions where double quantum dot regimes are found. These results show that overarching solutions for the tuning of quantum devices are enabled by machine learning.  \nBefore we can use a quantum computer we first need to be able to turn it on. There are many stages to this initial step, particularly for quantum computing architectures based on semiconductors. Silicon and SiGe devices can encode promising spin qubits1, demonstrating excellent fidelities, long coherence times and a pathway toscalability2–9. Many of these key characteristics revolve around the material itself providing the opportunity tobe purified to a near-perfect magnetically clean environment resulting in very weak to no hyperfine interactions. As the material of choice of the microelectronics industry, gate-defined quantum dots in silicon and SiGe have great potential for the fabrication of circuits consisting of a large number of qubits, an essential requirement to achieving a universal fault-tolerant quantum computer 10, 11.  \nMultiple gate electrodes provide the ability to tune differing devices into similar operating regimes. These gate voltages define a large parameter space to be explored. Each device architecture and material realisation defines a specific parameter space. The time-consuming challenge of tuning semiconductor devices becomes intractable as we combine different device architectures in the realisation of complex quantum circuits with millions ofcomponents. In some cases it may take human experts over 3 h to tune a double quantum dot device12. The development of machine learning algorithms for quantum device tuning12–27 is exceptionally challenging when looking for such overarching solutions. Multiple algorithms address different parts of the tuning problem such as finding double quantum dots13,22,25, and then the optimisation15, 16,21,24 and identification of transport features18, 19,26. A significant number of tuning algorithms have been developed for AlGaAs/GaAs double quantum dots13, 14, 16, 17,23. However, few have been demonstrated across different architectures nor on material compositions poised for scalability20,28, and only a small number of algorithms provide insight into the device parameter space12.  \nHere we demonstrate that it is possible to tune quantum dots in three different device architectures and material systems completely automatically. This machine learning-based algorithm, which we call ‘Cross-Architecture Tuning Solution using AI’ (CATSAI), requires only the following hyperparameters to be set once, for each type of device, in a configuration file: source-drain bias, safety voltage bounds, resolution and size of acquisition current maps and traces, the offset current noise floor, and Coulomb peak segmentation threshold (see Supplementary Material S1). The origin and gate voltage sweep directions can","cbCail5LHYEH0KwF","https://ap.wps.com/l/cbCail5LHYEH0KwF","pdf",2255741,1,10,"English","en",105,"# Overview\n# CATSAI algorithm and inputs\n## Hyperparameters and configuration\n## Signal processing and device setup\n# Demonstration across device architectures\n## FinFET, nanowire, and heterostructure\n# Outcomes and implications","[{\"question\":\"What problem does the work address in quantum device operation?\",\"answer\":\"Silicon and SiGe quantum devices suffer from variability that makes manual tuning slow and requires different protocols for each device realization.\"},{\"question\":\"How does CATSAI automate tuning across different quantum architectures?\",\"answer\":\"CATSAI uses one machine-learning algorithm that runs from scratch on multiple gate-defined devices, controlled by a small set of hyperparameters provided once per device type.\"},{\"question\":\"What additional insight does the algorithm provide beyond performing tuning?\",\"answer\":\"It characterizes the multidimensional parameter-space landscape, including the regions where double quantum dot regimes are found.\"}]","Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning | 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problem does the work address in quantum device operation?","Question",{"text":75,"@type":76},"Silicon and SiGe quantum devices suffer from variability that makes manual tuning slow and requires different protocols for each device realization.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does CATSAI automate tuning across different quantum architectures?",{"text":80,"@type":76},"CATSAI uses one machine-learning algorithm that runs from scratch on multiple gate-defined devices, controlled by a small set of hyperparameters provided once per device type.",{"name":82,"@type":73,"acceptedAnswer":83},"What additional insight does the algorithm provide beyond performing tuning?",{"text":84,"@type":76},"It characterizes the multidimensional parameter-space landscape, including the regions where double quantum dot regimes are 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