{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,8]],"date-time":"2025-06-08T22:28:39Z","timestamp":1749421719664,"version":"3.40.3"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030708658"},{"type":"electronic","value":"9783030708665"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-70866-5_11","type":"book-chapter","created":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T16:03:57Z","timestamp":1614701037000},"page":"167-183","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Three Quantum Machine Learning Approaches for Mobile User Indoor-Outdoor Detection"],"prefix":"10.1007","author":[{"given":"Frank","family":"Phillipson","sequence":"first","affiliation":[]},{"given":"Robert S.","family":"Wezeman","sequence":"additional","affiliation":[]},{"given":"Irina","family":"Chiscop","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,3]]},"reference":[{"issue":"4","key":"11_CR1","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1038\/nphys3272","volume":"11","author":"S Aaronson","year":"2015","unstructured":"Aaronson, S.: Read the fine print. Nat. Phys. 11(4), 291\u2013293 (2015)","journal-title":"Nat. Phys."},{"key":"11_CR2","unstructured":"Abohashima, Z., Elhosen, M., Houssein, E.H., Mohamed, W.M.: Classification with quantum machine learning: a survey. arXiv preprint arXiv:2006.12270 (2020)"},{"key":"11_CR3","doi-asserted-by":"publisher","first-page":"65877","DOI":"10.1109\/ACCESS.2019.2917592","volume":"7","author":"JL Bejarano-Luque","year":"2019","unstructured":"Bejarano-Luque, J.L., Toril, M., Fernandez-Navarro, M., Acedo-Hern\u00e1ndez, R., Luna-Ram\u00edrez, S.: A data-driven algorithm for indoor\/outdoor detection based on connection traces in a LTE network. IEEE Access 7, 65877\u201365888 (2019)","journal-title":"IEEE Access"},{"issue":"7671","key":"11_CR4","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/nature23474","volume":"549","author":"J Biamonte","year":"2017","unstructured":"Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., Lloyd, S.: Quantum machine learning. Nature 549(7671), 195\u2013202 (2017)","journal-title":"Nature"},{"key":"11_CR5","unstructured":"Booth, M., Reinhardt, S.P., Roy, A.: Partitioning optimization problems for hybrid classical\/quantum execution. Technical report, D-Wave Systems, September 2017"},{"key":"11_CR6","unstructured":"van den Brink, R.F., Phillipson, F., Neumann, N.M.P.: Vision on next level quantum software tooling. In: Computation Tools (2019)"},{"issue":"13","key":"11_CR7","doi-asserted-by":"publisher","first-page":"130501","DOI":"10.1103\/PhysRevLett.117.130501","volume":"117","author":"V Dunjko","year":"2016","unstructured":"Dunjko, V., Taylor, J.M., Briegel, H.J.: Quantum-enhanced machine learning. Phys. Rev. Lett. 117(13), 130501 (2016)","journal-title":"Phys. Rev. Lett."},{"key":"11_CR8","unstructured":"Erdbrink, R.: Analysis of UMTS cell assignment probabilities. Master\u2019s thesis, VU University Amsterdam, The Netherlands (2005)"},{"issue":"3","key":"11_CR9","doi-asserted-by":"publisher","first-page":"511","DOI":"10.3390\/s19030511","volume":"19","author":"A Esmaeili Kelishomi","year":"2019","unstructured":"Esmaeili Kelishomi, A., Garmabaki, A., Bahaghighat, M., Dong, J.: Mobile user indoor-outdoor detection through physical daily activities. Sensors 19(3), 511 (2019)","journal-title":"Sensors"},{"key":"11_CR10","unstructured":"Glover, F., Kochenberger, G., Du, Y.: A tutorial on formulating and using QUBO models. arXiv preprint arXiv:1811.11538 (2018)"},{"key":"11_CR11","doi-asserted-by":"publisher","unstructured":"Grover, L.K.: A fast quantum mechanical algorithm for database search. In: Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing, STOC 1996, pp. 212\u2013219. Association for Computing Machinery, New York (1996). https:\/\/doi.org\/10.1145\/237814.237866","DOI":"10.1145\/237814.237866"},{"issue":"7","key":"11_CR12","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1109\/JSAC.2009.090902","volume":"27","author":"M Haenggi","year":"2009","unstructured":"Haenggi, M., Andrews, J.G., Baccelli, F., Dousse, O., Franceschetti, M.: Stochastic geometry and random graphs for the analysis and design of wireless networks. IEEE J. Sel. Areas Commun. 27(7), 1029\u20131046 (2009)","journal-title":"IEEE J. Sel. Areas Commun."},{"issue":"15","key":"11_CR13","doi-asserted-by":"publisher","first-page":"150502","DOI":"10.1103\/PhysRevLett.103.150502","volume":"103","author":"AW Harrow","year":"2009","unstructured":"Harrow, A.W., Hassidim, A., Lloyd, S.: Quantum algorithm for linear systems of equations. Phys. Rev. Lett. 103(15), 150502 (2009)","journal-title":"Phys. Rev. Lett."},{"issue":"5","key":"11_CR14","doi-asserted-by":"publisher","first-page":"5355","DOI":"10.1103\/PhysRevE.58.5355","volume":"58","author":"T Kadowaki","year":"1998","unstructured":"Kadowaki, T., Nishimori, H.: Quantum annealing in the transverse Ising model. Phys. Rev. E 58(5), 5355 (1998)","journal-title":"Phys. Rev. E"},{"key":"11_CR15","unstructured":"Kapoor, A., Wiebe, N., Svore, K.: Quantum perceptron models. In: Advances in Neural Information Processing Systems, pp. 3999\u20134007 (2016)"},{"key":"11_CR16","doi-asserted-by":"publisher","unstructured":"Leiserson, C.E., et al.: There\u2019s plenty of room at the top: what will drive computer performance after Moore\u2019s law? Science 368(6495) (2020). https:\/\/doi.org\/10.1126\/SCIENCE.AAM974. Review summary in print version on page 1079: Computer Science","DOI":"10.1126\/SCIENCE.AAM974"},{"issue":"9","key":"11_CR17","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1038\/nphys3029","volume":"10","author":"S Lloyd","year":"2014","unstructured":"Lloyd, S., Mohseni, M., Rebentrost, P.: Quantum principal component analysis. Nat. Phys. 10(9), 631\u2013633 (2014)","journal-title":"Nat. Phys."},{"issue":"6","key":"11_CR18","doi-asserted-by":"publisher","first-page":"062315","DOI":"10.1103\/PhysRevA.89.062315","volume":"89","author":"GH Low","year":"2014","unstructured":"Low, G.H., Yoder, T.J., Chuang, I.L.: Quantum inference on Bayesian networks. Phys. Rev. A 89(6), 062315 (2014)","journal-title":"Phys. Rev. A"},{"key":"11_CR19","doi-asserted-by":"publisher","first-page":"5","DOI":"10.3389\/fphy.2014.00005","volume":"2","author":"A Lucas","year":"2014","unstructured":"Lucas, A.: Ising formulations of many NP problems. Front. Phys. 2, 5 (2014)","journal-title":"Front. Phys."},{"issue":"2","key":"11_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2200\/S00585ED1V01Y201407QMC008","volume":"5","author":"CC McGeoch","year":"2014","unstructured":"McGeoch, C.C.: Adiabatic quantum computation and quantum annealing: theory and practice. Synth. Lect. Quant. Comput. 5(2), 1\u201393 (2014)","journal-title":"Synth. Lect. Quant. Comput."},{"key":"11_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1007\/978-3-030-50433-5_43","volume-title":"Computational Science \u2013 ICCS 2020","author":"NMP Neumann","year":"2020","unstructured":"Neumann, N.M.P., de Heer, P.B.U.L., Chiscop, I., Phillipson, F.: Multi-agent reinforcement learning using simulated quantum annealing. In: Krzhizhanovskaya, V.V., et al. (eds.) ICCS 2020. LNCS, vol. 12142, pp. 562\u2013575. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-50433-5_43"},{"issue":"2","key":"11_CR22","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1007\/s42354-019-0164-0","volume":"3","author":"NMP Neumann","year":"2019","unstructured":"Neumann, N.M.P., Phillipson, F., Versluis, R.: Machine learning in the quantum era. Digitale Welt 3(2), 24\u201329 (2019)","journal-title":"Digitale Welt"},{"key":"11_CR23","unstructured":"Phillipson, F.: Quantum machine learning: benefits and practical examples. In: QANSWER, pp. 51\u201356 (2020)"},{"issue":"13","key":"11_CR24","doi-asserted-by":"publisher","first-page":"130503","DOI":"10.1103\/PhysRevLett.113.130503","volume":"113","author":"P Rebentrost","year":"2014","unstructured":"Rebentrost, P., Mohseni, M., Lloyd, S.: Quantum support vector machine for big data classification. Phys. Rev. Lett. 113(13), 130503 (2014)","journal-title":"Phys. Rev. Lett."},{"key":"11_CR25","unstructured":"Resch, S., Karpuzcu, U.R.: Quantum computing: an overview across the system stack. arXiv preprint arXiv:1905.07240 (2019)"},{"issue":"6195","key":"11_CR26","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1126\/science.1252319","volume":"345","author":"TF R\u00f8nnow","year":"2014","unstructured":"R\u00f8nnow, T.F., et al.: Defining and detecting quantum speedup. Science 345(6195), 420\u2013424 (2014)","journal-title":"Science"},{"key":"11_CR27","doi-asserted-by":"crossref","unstructured":"Saffar, I., Morel, M.L.A., Amara, M., Singh, K.D., Viho, C.: Mobile user environment detection using deep learning based multi-output classification. In: 2019 12th IFIP Wireless and Mobile Networking Conference (WMNC), pp. 16\u201323. IEEE (2019)","DOI":"10.23919\/WMNC.2019.8881474"},{"key":"11_CR28","doi-asserted-by":"crossref","unstructured":"Saffar, I., Morel, M.L.A., Singh, K.D., Viho, C.: Machine learning with partially labeled data for indoor outdoor detection. In: 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC), pp. 1\u20138. IEEE (2019)","DOI":"10.1109\/CCNC.2019.8651736"},{"issue":"6","key":"11_CR29","doi-asserted-by":"publisher","first-page":"60002","DOI":"10.1209\/0295-5075\/119\/60002","volume":"119","author":"M Schuld","year":"2017","unstructured":"Schuld, M., Fingerhuth, M., Petruccione, F.: Implementing a distance-based classifier with a quantum interference circuit. EPL (Europhys. Lett.) 119(6), 60002 (2017)","journal-title":"EPL (Europhys. Lett.)"},{"key":"11_CR30","series-title":"Quantum Science and Technology","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-96424-9","volume-title":"Supervised Learning with Quantum Computers","author":"M Schuld","year":"2018","unstructured":"Schuld, M., Petruccione, F.: Supervised Learning with Quantum Computers. QST. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-96424-9"},{"issue":"3","key":"11_CR31","doi-asserted-by":"publisher","first-page":"032308","DOI":"10.1103\/PhysRevA.101.032308","volume":"101","author":"M Schuld","year":"2020","unstructured":"Schuld, M., Bocharov, A., Svore, K.M., Wiebe, N.: Circuit-centric quantum classifiers. Phys. Rev. A 101(3), 032308 (2020)","journal-title":"Phys. Rev. A"},{"issue":"2","key":"11_CR32","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1080\/00107514.2014.964942","volume":"56","author":"M Schuld","year":"2015","unstructured":"Schuld, M., Sinayskiy, I., Petruccione, F.: An introduction to quantum machine learning. Contemp. Phys. 56(2), 172\u2013185 (2015)","journal-title":"Contemp. Phys."},{"issue":"3","key":"11_CR33","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1109\/TBME.2008.2002141","volume":"56","author":"T Tsalaile","year":"2008","unstructured":"Tsalaile, T., Sameni, R., Sanei, S., Jutten, C., Chambers, J., et al.: Sequential blind source extraction for quasi-periodic signals with time-varying period. IEEE Trans. Biomed. Eng. 56(3), 646\u2013655 (2008)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"10","key":"11_CR34","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.3390\/s16101563","volume":"16","author":"W Wang","year":"2016","unstructured":"Wang, W., Chang, Q., Li, Q., Shi, Z., Chen, W.: Indoor-outdoor detection using a smart phone sensor. Sensors 16(10), 1563 (2016)","journal-title":"Sensors"},{"key":"11_CR35","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s42354-019-0240-5","volume":"4","author":"R Wezeman","year":"2020","unstructured":"Wezeman, R., Neumann, N., Phillipson, F.: Distance-based classifier on the quantum inspire. Digitale Welt 4, 85\u201391 (2020). https:\/\/doi.org\/10.1007\/s42354-019-0240-5","journal-title":"Digitale Welt"},{"issue":"5","key":"11_CR36","doi-asserted-by":"publisher","first-page":"050505","DOI":"10.1103\/PhysRevLett.109.050505","volume":"109","author":"N Wiebe","year":"2012","unstructured":"Wiebe, N., Braun, D., Lloyd, S.: Quantum algorithm for data fitting. Phys. Rev. Lett. 109(5), 050505 (2012)","journal-title":"Phys. Rev. Lett."},{"key":"11_CR37","doi-asserted-by":"publisher","first-page":"107006","DOI":"10.1016\/j.cpc.2019.107006","volume":"248","author":"D Willsch","year":"2020","unstructured":"Willsch, D., Willsch, M., De Raedt, H., Michielsen, K.: Support vector machines on the D-wave quantum annealer. Comput. Phys. Commun. 248, 107006 (2020)","journal-title":"Comput. Phys. Commun."},{"key":"11_CR38","doi-asserted-by":"publisher","first-page":"63057","DOI":"10.1109\/ACCESS.2019.2914451","volume":"7","author":"L Zhang","year":"2019","unstructured":"Zhang, L., Ni, Q., Zhai, M., Moreno, J., Briso, C.: An ensemble learning scheme for indoor-outdoor classification based on KPIs of LTE network. IEEE Access 7, 63057\u201363065 (2019)","journal-title":"IEEE Access"},{"issue":"4","key":"11_CR39","doi-asserted-by":"publisher","first-page":"786","DOI":"10.3390\/s19040786","volume":"19","author":"Y Zhu","year":"2019","unstructured":"Zhu, Y., et al.: A fast indoor\/outdoor transition detection algorithm based on machine learning. Sensors 19(4), 786 (2019)","journal-title":"Sensors"}],"container-title":["Lecture Notes in Computer Science","Machine Learning for Networking"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-70866-5_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T16:11:17Z","timestamp":1614701477000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-70866-5_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030708658","9783030708665"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-70866-5_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"3 March 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning for Networking","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Paris","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mln2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.adda-association.org\/mln-2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"50","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"44% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the Corona pandemic this event was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}