{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:18:40Z","timestamp":1760141920799,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031304446"},{"type":"electronic","value":"9783031304453"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-30445-3_17","type":"book-chapter","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T09:02:52Z","timestamp":1682499772000},"page":"199-208","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Solving the\u00a0Traveling Salesman Problem with\u00a0a\u00a0Hybrid Quantum-Classical Feedforward Neural Network"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3189-7618","authenticated-orcid":false,"given":"Justyna","family":"Zawalska","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8032-7251","authenticated-orcid":false,"given":"Katarzyna","family":"Rycerz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,27]]},"reference":[{"key":"17_CR1","unstructured":"Brandao, F.G.S.L., Broughton, M., Farhi, E., Gutmann, S., Neven, H.: For fixed control parameters the quantum approximate optimization algorithm\u2019s objective function value concentrates for typical instances. https:\/\/arxiv.org\/abs\/1812.04170"},{"key":"17_CR2","doi-asserted-by":"publisher","unstructured":"Broughton, M., et al.: TensorFlow quantum: a software framework for quantum machine learning. https:\/\/doi.org\/10.48550\/ARXIV.2003.02989. https:\/\/arxiv.org\/abs\/2003.02989. Publisher: arXiv Version Number: 2","DOI":"10.48550\/ARXIV.2003.02989"},{"key":"17_CR3","unstructured":"Farhi, E., Goldstone, J., Gutmann, S.: A quantum approximate optimization algorithm. https:\/\/arxiv.org\/abs\/1411.4028"},{"key":"17_CR4","doi-asserted-by":"publisher","unstructured":"Farhi, E., Goldstone, J., Gutmann, S., Sipser, M.: Quantum computation by adiabatic evolution. https:\/\/doi.org\/10.48550\/ARXIV.QUANT-PH\/0001106. https:\/\/arxiv.org\/abs\/quant-ph\/0001106. Publisher: arXiv Version Number: 1","DOI":"10.48550\/ARXIV.QUANT-PH\/0001106"},{"key":"17_CR5","doi-asserted-by":"publisher","unstructured":"Khairy, S., Shaydulin, R., Cincio, L., Alexeev, Y., Balaprakash, P.: Learning to optimize variational quantum circuits to solve combinatorial problems, vol. 34, no. 3, pp. 2367\u20132375. https:\/\/doi.org\/10.1609\/aaai.v34i03.5616. https:\/\/arxiv.org\/abs\/1911.11071","DOI":"10.1609\/aaai.v34i03.5616"},{"key":"17_CR6","doi-asserted-by":"publisher","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. https:\/\/doi.org\/10.48550\/ARXIV.1412.6980. https:\/\/arxiv.org\/abs\/1412.6980. Publisher: arXiv Version Number: 9","DOI":"10.48550\/ARXIV.1412.6980"},{"key":"17_CR7","doi-asserted-by":"publisher","unstructured":"Lucas, A.: Ising formulations of many NP problems 2. https:\/\/doi.org\/10.3389\/fphy.2014.00005. https:\/\/journal.frontiersin.org\/article\/10.3389\/fphy.2014.00005\/abstract","DOI":"10.3389\/fphy.2014.00005"},{"key":"17_CR8","doi-asserted-by":"publisher","unstructured":"Luo, X.Z., Liu, J.G., Zhang, P., Wang, L.: Yao.jl: extensible, efficient framework for quantum algorithm design. https:\/\/doi.org\/10.48550\/ARXIV.1912.10877. https:\/\/arxiv.org\/abs\/1912.10877. Publisher: arXiv Version Number: 3","DOI":"10.48550\/ARXIV.1912.10877"},{"key":"17_CR9","doi-asserted-by":"publisher","unstructured":"Preskill, J.: Quantum computing in the NISQ era and beyond. https:\/\/doi.org\/10.48550\/ARXIV.1801.00862. https:\/\/arxiv.org\/abs\/1801.00862. Publisher: arXiv Version Number: 3","DOI":"10.48550\/ARXIV.1801.00862"},{"key":"17_CR10","doi-asserted-by":"publisher","unstructured":"Salehi, O., Glos, A., Miszczak, J.A.: Unconstrained binary models of the travelling salesman problem variants for quantum optimization. Quantum Inf. Process. 21(2), 67 (2022). https:\/\/doi.org\/10.1007\/s11128-021-03405-5. https:\/\/link.springer.com\/10.1007\/s11128-021-03405-5","DOI":"10.1007\/s11128-021-03405-5"},{"key":"17_CR11","unstructured":"Verdon, G., et al.: Learning to learn with quantum neural networks via classical neural networks. https:\/\/arxiv.org\/abs\/1907.05415"},{"key":"17_CR12","doi-asserted-by":"publisher","unstructured":"Wecker, D., Hastings, M.B., Troyer, M.: Training a quantum optimizer. Phys. Rev. A 94(2), 022309 (2016). https:\/\/doi.org\/10.1103\/PhysRevA.94.022309. https:\/\/arxiv.org\/abs\/1605.05370","DOI":"10.1103\/PhysRevA.94.022309"},{"key":"17_CR13","doi-asserted-by":"publisher","unstructured":"Wilson, M., Stromswold, R., Wudarski, F., Hadfield, S., Tubman, N.M., Rieffel, E.G.: Optimizing quantum heuristics with meta-learning. Quantum Mach. Intell. 3(1), 13 (2021). https:\/\/doi.org\/10.1007\/s42484-020-00022-w. https:\/\/link.springer.com\/10.1007\/s42484-020-00022-w","DOI":"10.1007\/s42484-020-00022-w"},{"key":"17_CR14","doi-asserted-by":"publisher","unstructured":"Zhou, L., Wang, S.T., Choi, S., Pichler, H., Lukin, M.D.: Quantum approximate optimization algorithm: performance, mechanism, and implementation on near-term devices. Phys. Rev. X 10(2), 021067 (2020). https:\/\/doi.org\/10.1103\/PhysRevX.10.021067. https:\/\/arxiv.org\/abs\/1812.01041","DOI":"10.1103\/PhysRevX.10.021067"}],"container-title":["Lecture Notes in Computer Science","Parallel Processing and Applied Mathematics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-30445-3_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T04:45:00Z","timestamp":1760071500000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-30445-3_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031304446","9783031304453"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-30445-3_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"27 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PPAM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Parallel Processing and Applied Mathematics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gdansk","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ppam2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ppam.edu.pl\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}