{"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/a-hybrid-machine-learning-algorithm-for","title":"A hybrid machine-learning algorithm for designing quantum experiments","arxiv_id":"1812.03183","date":"2018-12-07","proceeding":null,"authors":["L. O'Driscoll","R. Nichols","P. A. Knott"],"abstract":"We introduce a hybrid machine-learning algorithm for designing quantum optics\nexperiments that produce specific quantum states. Our algorithm successfully\nfound experimental schemes to produce all 5 states we asked it to, including\nSchr\\\"odinger cat states and cubic phase states, all to a fidelity of over\n$96\\%$. Here we specifically focus on designing realistic experiments, and\nhence all of the algorithm's designs only contain experimental elements that\nare available with current technology. The core of our algorithm is a genetic\nalgorithm that searches for optimal arrangements of the experimental elements,\nbut to speed up the initial search we incorporate a neural network that\nclassifies quantum states. The latter is of independent interest, as it quickly\nlearned to accurately classify quantum states given their photon-number\ndistributions.","url_abs":"http://arxiv.org/abs/1812.03183v2","url_pdf":"http://arxiv.org/pdf/1812.03183v2.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":"a-hybrid-machine-learning-algorithm-for","repo_url":"https://github.com/lewis-od/Quantum-Optics","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"all","task_name":"All"},{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"hybrid-machine-learning","task_name":"Hybrid Machine Learning"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}