{"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/boosting-on-the-shoulders-of-giants-in","title":"Boosting on the shoulders of giants in quantum device calibration","arxiv_id":"2005.06194","date":"2020-05-13","proceeding":null,"authors":["Alex Wozniakowski","Jayne Thompson","Mile Gu","Felix Binder"],"abstract":"Traditional machine learning applications, such as optical character recognition, arose from the inability to explicitly program a computer to perform a routine task. In this context, learning algorithms usually derive a model exclusively from the evidence present in a massive dataset. Yet in some scientific disciplines, obtaining an abundance of data is an impractical luxury, however; there is an explicit model of the domain based upon previous scientific discoveries. Here we introduce a new approach to machine learning that is able to leverage prior scientific discoveries in order to improve generalizability over a scientific model. We show its efficacy in predicting the entire energy spectrum of a Hamiltonian on a superconducting quantum device, a key task in present quantum computer calibration. Our accuracy surpasses the current state-of-the-art by over $20\\%.$ Our approach thus demonstrates how artificial intelligence can be further enhanced by \"standing on the shoulders of giants.\"","url_abs":"https://arxiv.org/abs/2005.06194v1","url_pdf":"https://arxiv.org/pdf/2005.06194v1.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":"boosting-on-the-shoulders-of-giants-in","repo_url":"https://github.com/a-wozniakowski/scikit-physlearn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"multi-target-regression","task_name":"Multi-target regression"},{"task_slug":"optical-character-recognition","task_name":"Optical Character Recognition"}],"methods":[{"method_slug":"base-boosting","method_name":"Base Boosting"}],"datasets_introduced":[],"methods_introduced":[{"slug":"base-boosting","name":"Base Boosting","full_name":"Base Boosting"}],"results":[{"leaderboard":"/sota/multi-target-regression-on-google-5-qubit","task":"Multi-target regression","dataset":"Google 5 qubit random Hamiltonian","model":"Base boosting","rank_in_archive_order":1,"of":1,"metrics":{"Average mean absolute error":"1.05"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}