{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T08:48:43Z","timestamp":1777106923066,"version":"3.51.4"},"reference-count":38,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"PPI IoT\/Manufatura 4.0","award":["053\/2023"],"award-info":[{"award-number":["053\/2023"]}]},{"name":"PPI IoT\/Manufatura 4.0","award":["APP0041\/2023"],"award-info":[{"award-number":["APP0041\/2023"]}]},{"name":"PPI IoT\/Manufatura 4.0","award":["PPP0006\/2024"],"award-info":[{"award-number":["PPP0006\/2024"]}]},{"DOI":"10.13039\/501100006181","name":"FAPESB","doi-asserted-by":"publisher","award":["053\/2023"],"award-info":[{"award-number":["053\/2023"]}],"id":[{"id":"10.13039\/501100006181","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006181","name":"FAPESB","doi-asserted-by":"publisher","award":["APP0041\/2023"],"award-info":[{"award-number":["APP0041\/2023"]}],"id":[{"id":"10.13039\/501100006181","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006181","name":"FAPESB","doi-asserted-by":"publisher","award":["PPP0006\/2024"],"award-info":[{"award-number":["PPP0006\/2024"]}],"id":[{"id":"10.13039\/501100006181","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Forecasting demand for assets and services can be addressed in various markets, providing a competitive advantage when the predictive models used demonstrate high accuracy. However, the training of machine learning models incurs high computational costs, which may limit the training of prediction models based on available computational capacity. In this context, this paper presents an approach for training demand prediction models using quantum neural networks. For this purpose, a quantum neural network was used to forecast demand for vehicle financing. A classical recurrent neural network was used to compare the results, and they show a similar predictive capacity between the classical and quantum models, with the advantage of using a lower number of training parameters and also converging in fewer steps. Utilizing quantum computing techniques offers a promising solution to overcome the limitations of traditional machine learning approaches in training predictive models for complex market dynamics.<\/jats:p>","DOI":"10.3390\/e27050490","type":"journal-article","created":{"date-parts":[[2025,5,2]],"date-time":"2025-05-02T07:44:58Z","timestamp":1746171898000},"page":"490","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Exploring Quantum Neural Networks for Demand Forecasting"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-9076-0012","authenticated-orcid":false,"given":"Gleydson Fernandes","family":"de Jesus","sequence":"first","affiliation":[{"name":"QuIIN\u2013Quantum Industrial Innovation, EMBRAPII CIMATEC Competence Center in Quantum Technologies, SENAI CIMATEC, Av. Orlando Gomes, Salvador 41650-010, Bahia, Brazil"},{"name":"Latin America Quantum Computing Center, SENAI CIMATEC, Salvador 41650-010, Bahia, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9553-7988","authenticated-orcid":false,"given":"Maria Helo\u00edsa Fraga","family":"da Silva","sequence":"additional","affiliation":[{"name":"QuIIN\u2013Quantum Industrial Innovation, EMBRAPII CIMATEC Competence Center in Quantum Technologies, SENAI CIMATEC, Av. Orlando Gomes, Salvador 41650-010, Bahia, Brazil"},{"name":"Latin America Quantum Computing Center, SENAI CIMATEC, Salvador 41650-010, Bahia, Brazil"},{"name":"Grupo de Informa\u00e7\u00e3o Qu\u00e2ntica e F\u00edsica Estat\u00edstica, Centro de Ci\u00eancias Exatas e das Tecnologias, Universidade Federal do Oeste da Bahia, Barreiras 47810-059, Bahia, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5051-7241","authenticated-orcid":false,"given":"Otto Menegasso","family":"Pires","sequence":"additional","affiliation":[{"name":"QuIIN\u2013Quantum Industrial Innovation, EMBRAPII CIMATEC Competence Center in Quantum Technologies, SENAI CIMATEC, Av. Orlando Gomes, Salvador 41650-010, Bahia, Brazil"},{"name":"Latin America Quantum Computing Center, SENAI CIMATEC, Salvador 41650-010, Bahia, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9184-3710","authenticated-orcid":false,"given":"Lucas Cruz","family":"da Silva","sequence":"additional","affiliation":[{"name":"Robotics Department, SENAI CIMATEC, Salvador 41650-010, Bahia, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3318-1111","authenticated-orcid":false,"given":"Clebson","family":"dos Santos Cruz","sequence":"additional","affiliation":[{"name":"Grupo de Informa\u00e7\u00e3o Qu\u00e2ntica e F\u00edsica Estat\u00edstica, Centro de Ci\u00eancias Exatas e das Tecnologias, Universidade Federal do Oeste da Bahia, Barreiras 47810-059, Bahia, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5466-7933","authenticated-orcid":false,"given":"Val\u00e9ria Loureiro","family":"da Silva","sequence":"additional","affiliation":[{"name":"QuIIN\u2013Quantum Industrial Innovation, EMBRAPII CIMATEC Competence Center in Quantum Technologies, SENAI CIMATEC, Av. Orlando Gomes, Salvador 41650-010, Bahia, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.jmoneco.2018.05.001","article-title":"Financial regimes and uncertainty shocks","volume":"101","author":"Alessandri","year":"2019","journal-title":"J. Monet. Econ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105840","DOI":"10.1016\/j.jeconbus.2019.04.001","article-title":"The impact of uncertainty on the number of businesses","volume":"105","author":"Ghosal","year":"2019","journal-title":"J. Econ. Bus."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1080\/09538259.2010.510317","article-title":"Financial uncertainty and business investment","volume":"22","author":"Stockhammer","year":"2010","journal-title":"Rev. Political Econ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1007\/s11831-020-09413-5","article-title":"Stock market forecasting using computational intelligence: A survey","volume":"28","author":"Kumar","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"154","DOI":"10.54097\/fbem.v11i3.13209","article-title":"Research on the management innovation of smes from the perspective of strategic management","volume":"11","author":"Guo","year":"2023","journal-title":"Front. Bus. Econ. Manag."},{"key":"ref_6","first-page":"3037","article-title":"Comparison of statistical and machine learning methods for daily sku demand forecasting","volume":"22","author":"Spiliotis","year":"2020","journal-title":"Oper. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"106435","DOI":"10.1016\/j.cie.2020.106435","article-title":"An optimized model using lstm network for demand forecasting","volume":"143","author":"Abbasimehr","year":"2020","journal-title":"Comput. Ind. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.1007\/s10845-021-01737-8","article-title":"Demand forecasting application with regression and artificial intelligence methods in a construction machinery company","volume":"32","author":"Aktepe","year":"2021","journal-title":"J. Intell. Manuf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3803","DOI":"10.1287\/mnsc.2020.3667","article-title":"Demand modeling in the presence of unobserved lost sales","volume":"67","author":"Subramanian","year":"2020","journal-title":"Manag. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1287\/msom.2015.0561","article-title":"Analytics for an online retailer: Demand forecasting and price optimization","volume":"18","author":"Ferreira","year":"2016","journal-title":"Manuf. Serv. Oper. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3623","DOI":"10.1002\/qj.3863","article-title":"Beyond skill scores: Exploring sub-seasonal forecast value through a case-study of french month-ahead energy prediction","volume":"146","author":"Dorrington","year":"2020","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106380","DOI":"10.1016\/j.cie.2020.106380","article-title":"Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion","volume":"142","author":"Abolghasemi","year":"2020","journal-title":"Comput. Ind. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Aamer, A., Yani, L.E., and Priyatna, I.A. (2020). Data analytics in the supply chain management: Review of machine learning applications in demand forecasting. Oper. Supply Chain Manag. An Int. J., 14.","DOI":"10.31387\/oscm0440281"},{"key":"ref_14","unstructured":"Kerkk\u00e4nen, A. (2010). Improving Demand Forecasting Practices in the Industrial Context, Lappeenranta University of Technology."},{"key":"ref_15","first-page":"1","article-title":"Machine learning for demand forecasting in manufacturing","volume":"6","author":"Jeyaraman","year":"2024","journal-title":"Int. J. Multidiscip. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1080\/13675567.2020.1803246","article-title":"Machine learning demand forecasting and supply chain performance","volume":"25","author":"Feizabadi","year":"2020","journal-title":"Int. J. Logistics Res. Appl."},{"key":"ref_17","first-page":"6657","article-title":"Combining predictive base models using deep ensemble learning","volume":"39","author":"Oner","year":"2020","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"20190143","DOI":"10.1098\/rsfs.2019.0143","article-title":"Quantum computing using continuous-time evolution","volume":"10","author":"Kendon","year":"2020","journal-title":"Interface Focus"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1038\/s43588-022-00311-3","article-title":"Challenges and opportunities in quantum machine learning","volume":"2","author":"Cerezo","year":"2022","journal-title":"Nat. Comput. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"116512","DOI":"10.1016\/j.eswa.2022.116512","article-title":"Machine learning in the quantum realm: The state-of-the-art, challenges, and future vision","volume":"194","author":"Houssein","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_21","first-page":"2165452","article-title":"Quantum machine learning: From physics to software engineering","volume":"8","author":"Melnikov","year":"2023","journal-title":"Adv. Phys. X"},{"key":"ref_22","first-page":"20170551","article-title":"Quantum machine learning: A classical perspective","volume":"474","author":"Ciliberto","year":"2017","journal-title":"Proc. Math. Phys. Eng. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"032430","DOI":"10.1103\/PhysRevA.103.032430","article-title":"Effect of data encoding on the expressive power of variational quantum-machine-learning models","volume":"103","author":"Schuld","year":"2021","journal-title":"Phys. Rev. A"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4919","DOI":"10.1038\/s41467-022-32550-3","article-title":"Generalization in quantum machine learning from few training data","volume":"13","author":"Caro","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.22331\/q-2023-11-29-1191","article-title":"Quantum Deep Hedging","volume":"7","author":"Cherrat","year":"2023","journal-title":"Quantum"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"115678","DOI":"10.1016\/j.eswa.2021.115678","article-title":"Fully component selection: An efficient combination of feature selection and principal component analysis to increase model performance","volume":"186","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"102578","DOI":"10.1016\/j.jvcir.2019.102578","article-title":"Dimension reduction of image deep feature using PCA","volume":"63","author":"Ma","year":"2019","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Avramouli, M., Savvas, I., Garani, G., and Vasilaki, A. (2021, January 26\u201328). Quantum machine learning: Current state and challenges. Proceedings of the 25th Pan-Hellenic Conference on Informatics, PCI \u201921, Volos, Greece.","DOI":"10.1145\/3503823.3503896"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1016\/j.psep.2024.04.032","article-title":"Quantum machine learning for drowsiness detection with EEG signals","volume":"186","author":"Lins","year":"2024","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_30","unstructured":"Combarro, E., and Gonzalez-Castillo, S. (2023). A Practical Guide to Quantum Machine Learning and Quantum Optimization: Hands-on Approach to Modern Quantum Algorithms, Packt Publishing. [1st ed.]."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Schuld, M., and Petruccione, F. (2021). Machine Learning with Quantum Computers, Springer.","DOI":"10.1007\/978-3-030-83098-4"},{"key":"ref_32","first-page":"181","article-title":"The effect of superposition and entanglement on hybrid quantum machine learning for weather forecasting","volume":"23","author":"Ogur","year":"2023","journal-title":"Quantum Inf. Comput."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1038\/s41567-019-0648-8","article-title":"Quantum convolutional neural networks","volume":"15","author":"Cong","year":"2018","journal-title":"Nat. Phys."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/S0020-0255(00)00055-4","article-title":"Quantum artificial neural network architectures and components","volume":"128","author":"Narayanan","year":"2000","journal-title":"Inf. Sci."},{"key":"ref_35","unstructured":"(2023, November 24). Sklearn. Available online: https:\/\/scikit-learn.org\/stable\/index.html."},{"key":"ref_36","unstructured":"(2024, July 31). Pennylane. Available online: https:\/\/pennylane.ai\/."},{"key":"ref_37","unstructured":"(2024, July 31). Xanadu. Available online: https:\/\/www.xanadu.ai\/."},{"key":"ref_38","unstructured":"(2024, July 31). Tensorflow. Available online: https:\/\/www.tensorflow.org."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/5\/490\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:25:56Z","timestamp":1760030756000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/5\/490"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,1]]},"references-count":38,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["e27050490"],"URL":"https:\/\/doi.org\/10.3390\/e27050490","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,1]]}}}