{"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/supervised-learning-with-quantum-enhanced","title":"Supervised learning with quantum enhanced feature spaces","arxiv_id":"1804.11326","date":"2018-04-30","proceeding":null,"authors":["Vojtech Havlicek","Antonio D. Córcoles","Kristan Temme","Aram W. Harrow","Abhinav Kandala","Jerry M. Chow","Jay M. Gambetta"],"abstract":"Machine learning and quantum computing are two technologies each with the\npotential for altering how computation is performed to address previously\nuntenable problems. Kernel methods for machine learning are ubiquitous for\npattern recognition, with support vector machines (SVMs) being the most\nwell-known method for classification problems. However, there are limitations\nto the successful solution to such problems when the feature space becomes\nlarge, and the kernel functions become computationally expensive to estimate. A\ncore element to computational speed-ups afforded by quantum algorithms is the\nexploitation of an exponentially large quantum state space through controllable\nentanglement and interference. Here, we propose and experimentally implement\ntwo novel methods on a superconducting processor. Both methods represent the\nfeature space of a classification problem by a quantum state, taking advantage\nof the large dimensionality of quantum Hilbert space to obtain an enhanced\nsolution. One method, the quantum variational classifier builds on [1,2] and\noperates through using a variational quantum circuit to classify a training set\nin direct analogy to conventional SVMs. In the second, a quantum kernel\nestimator, we estimate the kernel function and optimize the classifier\ndirectly. The two methods present a new class of tools for exploring the\napplications of noisy intermediate scale quantum computers [3] to machine\nlearning.","url_abs":"http://arxiv.org/abs/1804.11326v2","url_pdf":"http://arxiv.org/pdf/1804.11326v2.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":"supervised-learning-with-quantum-enhanced","repo_url":"https://github.com/andre-juan/good_quantum_kernels","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.11326","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}