{"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/efficient-learning-for-deep-quantum-neural","title":"Efficient Learning for Deep Quantum Neural Networks","arxiv_id":"1902.10445","date":"2019-02-27","proceeding":null,"authors":["Kerstin Beer","Dmytro Bondarenko","Terry Farrelly","Tobias J. Osborne","Robert Salzmann","Ramona Wolf"],"abstract":"Neural networks enjoy widespread success in both research and industry and,\nwith the imminent advent of quantum technology, it is now a crucial challenge\nto design quantum neural networks for fully quantum learning tasks. Here we\npropose the use of quantum neurons as a building block for quantum feed-forward\nneural networks capable of universal quantum computation. We describe the\nefficient training of these networks using the fidelity as a cost function and\nprovide both classical and efficient quantum implementations. Our method allows\nfor fast optimisation with reduced memory requirements: the number of qudits\nrequired scales with only the width, allowing the optimisation of deep\nnetworks. We benchmark our proposal for the quantum task of learning an unknown\nunitary and find remarkable generalisation behaviour and a striking robustness\nto noisy training data.","url_abs":"http://arxiv.org/abs/1902.10445v1","url_pdf":"http://arxiv.org/pdf/1902.10445v1.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":"efficient-learning-for-deep-quantum-neural","repo_url":"https://github.com/R8monaW/DeepQNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"efficient-learning-for-deep-quantum-neural","repo_url":"https://github.com/peixinshen/DeepQuantumNeuralNetworks-Mathematica","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1902.10445","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}