{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T11:27:15Z","timestamp":1740137235157,"version":"3.37.3"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T00:00:00Z","timestamp":1721001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T00:00:00Z","timestamp":1721001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Quantum Inf Process"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Certifying whether an arbitrary quantum system is entangled or not, is, in general, an NP-hard problem. Though various necessary and sufficient conditions have already been explored in this regard for lower-dimensional systems, it is hard to extend them to higher dimensions. Recently, an ensemble bagging and convex hull approximation (CHA) approach (together, BCHA) was proposed and it strongly suggests employing a machine learning technique for the separability-entanglement classification problem. However, BCHA does only incorporate the balanced dataset for classification tasks which results in lower average accuracy. In order to solve the data imbalance problem in the present literature, an exploration of the boosting technique has been carried out, and a trade-off between the boosting and bagging-based ensemble classifier is explored for quantum separability problems. For the two-qubit and two-qutrit quantum systems, the pros and cons of the proposed random under-sampling boost CHA (RUSBCHA) for the quantum separability problem are compared with the state-of-the-art CHA and BCHA approaches. As the data are highly unbalanced, performance measures such as overall accuracy, average accuracy, F-measure, and G-mean are evaluated for a fair comparison. The outcomes suggest that RUSBCHA is an alternative to the BCHA approach. Also, for several cases, performance improvements are observed for RUSBCHA since the data are imbalanced.<\/jats:p>","DOI":"10.1007\/s11128-024-04469-9","type":"journal-article","created":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T07:02:02Z","timestamp":1721026922000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Trade-off between bagging and boosting for quantum separability-entanglement classification"],"prefix":"10.1007","volume":"23","author":[{"given":"Sanuja D.","family":"Mohanty","sequence":"first","affiliation":[]},{"given":"Ram N.","family":"Patro","sequence":"additional","affiliation":[]},{"given":"Pradyut K.","family":"Biswal","sequence":"additional","affiliation":[]},{"given":"Biswajit","family":"Pradhan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3117-0785","authenticated-orcid":false,"given":"Sk","family":"Sazim","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,15]]},"reference":[{"key":"4469_CR1","doi-asserted-by":"publisher","first-page":"012315","DOI":"10.1103\/PhysRevA.98.012315","volume":"98","author":"S Lu","year":"2018","unstructured":"Lu, S., Huang, S., Li, K., Li, J., Chen, J., Lu, D., Ji, Z., Shen, Y., Zhou, D., Zeng, B.: Separability-entanglement classifier via machine learning. Phys. Rev. A 98, 012315 (2018)","journal-title":"Phys. Rev. A"},{"key":"4469_CR2","doi-asserted-by":"publisher","first-page":"045001","DOI":"10.1088\/1367-2630\/ab783d","volume":"22","author":"C Harney","year":"2020","unstructured":"Harney, C., Pirandola, S., Ferraro, A., Paternostro, M.: Entanglement classification via neural network quantum states. New J. Phys. 22, 045001 (2020). https:\/\/doi.org\/10.1088\/1367-2630\/ab783d","journal-title":"New J. Phys."},{"key":"4469_CR3","doi-asserted-by":"publisher","first-page":"033278","DOI":"10.1103\/PhysRevResearch.3.033278","volume":"3","author":"S Ahmed","year":"2021","unstructured":"Ahmed, S., S\u00e1nchez Mu\u00f1oz, C., Nori, F., Kockum, A.F.: Classification and reconstruction of optical quantum states with deep neural networks. Phys. Rev. Res. 3, 033278 (2021). https:\/\/doi.org\/10.1103\/PhysRevResearch.3.033278","journal-title":"Phys. Rev. Res."},{"key":"4469_CR4","doi-asserted-by":"publisher","first-page":"140502","DOI":"10.1103\/PhysRevLett.127.140502","volume":"127","author":"S Ahmed","year":"2021","unstructured":"Ahmed, S., S\u00e1nchez Mu\u00f1oz, C., Nori, F., Kockum, A.F.: Quantum state tomography with conditional generative adversarial networks. Phys. Rev. Lett. 127, 140502 (2021). https:\/\/doi.org\/10.1103\/PhysRevLett.127.140502","journal-title":"Phys. Rev. Lett."},{"key":"4469_CR5","doi-asserted-by":"publisher","first-page":"062334","DOI":"10.1103\/PhysRevA.100.062334","volume":"100","author":"W Wang","year":"2019","unstructured":"Wang, W., Lo, H.-K.: Machine learning for optimal parameter prediction in quantum key distribution. Phys. Rev. A 100, 062334 (2019). https:\/\/doi.org\/10.1103\/PhysRevA.100.062334","journal-title":"Phys. Rev. A"},{"key":"4469_CR6","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1038\/s41534-019-0141-3","volume":"5","author":"MY Niu","year":"2019","unstructured":"Niu, M.Y., Boixo, S., Smelyanskiy, V.N., Neven, H.: Universal quantum control through deep reinforcement learning. NPJ Quantum Inf. 5, 33 (2019). https:\/\/doi.org\/10.1038\/s41534-019-0141-3","journal-title":"NPJ Quantum Inf."},{"issue":"1","key":"4469_CR7","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1038\/s41534-019-0201-8","volume":"5","author":"XM Zhang","year":"2019","unstructured":"Zhang, X.M., Wei, Z., Asad, R., Yang, X.C., Wang, X.: When does reinforcement learning stand out in quantum control? A comparative study on state preparation. NPJ Quantum Inf. 5(1), 85 (2019)","journal-title":"NPJ Quantum Inf."},{"key":"4469_CR8","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1038\/s42005-019-0169-x","volume":"2","author":"R Porotti","year":"2019","unstructured":"Porotti, R., Tamascelli, D., Restelli, M., Prati, E.: Coherent transport of quantum states by deep reinforcement learning. Commun. Phys. 2, 61 (2019)","journal-title":"Commun. Phys."},{"key":"4469_CR9","doi-asserted-by":"publisher","first-page":"031086","DOI":"10.1103\/PhysRevX.8.031086","volume":"8","author":"M Bukov","year":"2018","unstructured":"Bukov, M., Day, A.G.R., Sels, D., Weinberg, P., Polkovnikov, A., Mehta, P.: Reinforcement learning in different phases of quantum control. Phys. Rev. X 8, 031086 (2018). https:\/\/doi.org\/10.1103\/PhysRevX.8.031086","journal-title":"Phys. Rev. X"},{"key":"4469_CR10","doi-asserted-by":"publisher","first-page":"L040401","DOI":"10.1103\/PhysRevA.103.L040401","volume":"103","author":"Y Ding","year":"2021","unstructured":"Ding, Y., Ban, Y., Mart\u00edn-Guerrero, J.D., Solano, E., Casanova, J., Chen, X.: Breaking adiabatic quantum control with deep learning. Phys. Rev. A 103, L040401 (2021). https:\/\/doi.org\/10.1103\/PhysRevA.103.L040401","journal-title":"Phys. Rev. A"},{"key":"4469_CR11","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1038\/s41534-020-00302-0","volume":"6","author":"C C\u00eerstoiu","year":"2020","unstructured":"C\u00eerstoiu, C., Holmes, Z., Iosue, J., Cincio, L., Coles, P.J., Sornborger, A.: Variational fast forwarding for quantum simulation beyond the coherence time. NPJ Quantum Inf. 6, 82 (2020)","journal-title":"NPJ Quantum Inf."},{"key":"4469_CR12","doi-asserted-by":"publisher","first-page":"035001","DOI":"10.1088\/1367-2630\/ab6f1f","volume":"22","author":"J Schuff","year":"2020","unstructured":"Schuff, J., Fiderer, L.J., Braun, D.: Improving the dynamics of quantum sensors with reinforcement learning. New J. Phys. 22, 035001 (2020). https:\/\/doi.org\/10.1088\/1367-2630\/ab6f1f","journal-title":"New J. Phys."},{"key":"4469_CR13","unstructured":"Lohani, S., Lukens, J.\u00a0M., Glasser, R.\u00a0T., Searles, T.\u00a0A., Kirby, B.\u00a0T.: Data-centric machine learning in quantum information science, arXiv e-prints , eid arXiv:2201.09134 (2022), arXiv:2201.09134 [quant-ph]"},{"key":"4469_CR14","doi-asserted-by":"publisher","unstructured":"Gurvits, L.: Classical deterministic complexity of Edmonds\u2019 problem and quantum entanglement, in Proceedings of the Thirty-Fifth Annual ACM Symposium on Theory of Computing, series and number STOC \u201903 (Association for Computing Machinery, New York, NY, USA, 2003) p. 10\u201319 https:\/\/doi.org\/10.1145\/780542.780545","DOI":"10.1145\/780542.780545"},{"key":"4469_CR15","doi-asserted-by":"publisher","first-page":"1413","DOI":"10.1103\/PhysRevLett.77.1413","volume":"77","author":"A Peres","year":"1996","unstructured":"Peres, A.: Separability criterion for density matrices. Phys. Rev. Lett. 77, 1413 (1996)","journal-title":"Phys. Rev. Lett."},{"key":"4469_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/s0375-9601(96)00706-2","volume":"223","author":"M Horodecki","year":"1996","unstructured":"Horodecki, M., Horodecki, P., Horodecki, R.: Separability of mixed states: necessary and sufficient conditions. Phys. Lett. A 223, 1\u20138 (1996). https:\/\/doi.org\/10.1016\/s0375-9601(96)00706-2","journal-title":"Phys. Lett. A"},{"key":"4469_CR17","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1103\/RevModPhys.81.865","volume":"81","author":"R Horodecki","year":"2009","unstructured":"Horodecki, R., Horodecki, P., Horodecki, M., Horodecki, K.: Quantum entanglement. Rev. Mod. Phys. 81, 865 (2009). https:\/\/doi.org\/10.1103\/RevModPhys.81.865","journal-title":"Rev. Mod. Phys."},{"key":"4469_CR18","doi-asserted-by":"publisher","first-page":"187904","DOI":"10.1103\/PhysRevLett.88.187904","volume":"88","author":"AC Doherty","year":"2002","unstructured":"Doherty, A.C., Parrilo, P.A., Spedalieri, F.M.: Distinguishing separable and entangled states. Phys. Rev. Lett. 88, 187904 (2002). https:\/\/doi.org\/10.1103\/PhysRevLett.88.187904","journal-title":"Phys. Rev. Lett."},{"key":"4469_CR19","doi-asserted-by":"publisher","first-page":"052306","DOI":"10.1103\/PhysRevA.80.052306","volume":"80","author":"M Navascu\u00e9s","year":"2009","unstructured":"Navascu\u00e9s, M., Owari, M., Plenio, M.B.: Power of symmetric extensions for entanglement detection. Phys. Rev. A 80, 052306 (2009). https:\/\/doi.org\/10.1103\/PhysRevA.80.052306","journal-title":"Phys. Rev. A"},{"key":"4469_CR20","doi-asserted-by":"publisher","first-page":"063033","DOI":"10.1088\/1367-2630\/ac0388","volume":"23","author":"C Harney","year":"2021","unstructured":"Harney, C., Paternostro, M., Pirandola, S.: Mixed state entanglement classification using artificial neural networks. New J. Phys. 23, 063033 (2021)","journal-title":"New J. Phys."},{"key":"4469_CR21","doi-asserted-by":"publisher","first-page":"023238","DOI":"10.1103\/PhysRevResearch.4.023238","volume":"4","author":"A Girardin","year":"2022","unstructured":"Girardin, A., Brunner, N., Kriv\u00e1chy, T.: Building separable approximations for quantum states via neural networks. Phys. Rev. Res. 4, 023238 (2022)","journal-title":"Phys. Rev. Res."},{"key":"4469_CR22","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/BF00058655","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman, L.: Bagging predictors. Mach. Learn. 24, 123 (1996). https:\/\/doi.org\/10.1007\/BF00058655","journal-title":"Mach. Learn."},{"key":"4469_CR23","doi-asserted-by":"publisher","first-page":"4996","DOI":"10.1080\/01431161.2019.1577580","volume":"40","author":"RN Patro","year":"2019","unstructured":"Patro, R.N., Subudhi, S., Biswal, P.K., Dell\u2019Acqua, F.: Dictionary-based classifiers for exploiting feature sequence information and their application to hyperspectral remotely sensed data. Int. J. Remote Sens. 40, 4996 (2019)","journal-title":"Int. J. Remote Sens."},{"key":"4469_CR24","doi-asserted-by":"publisher","first-page":"9279","DOI":"10.1080\/01431161.2019.1629717","volume":"40","author":"RN Patro","year":"2019","unstructured":"Patro, R.N., Subudhi, S., Biswal, P.K., Dell\u2019Acqua, F., Sahoo, H.K.: Conditional nearest regularized subspace classifiers: a fast classification approach for HSI. Int. J. Remote Sens. 40, 9279 (2019)","journal-title":"Int. J. Remote Sens."},{"key":"4469_CR25","first-page":"3","volume":"160","author":"SB Kotsiantis","year":"2007","unstructured":"Kotsiantis, S.B., Zaharakis, I., Pintelas, P., et al.: Supervised machine learning: a review of classification techniques. Emerg. Artif. Intell. Appl. Comput. Eng. 160, 3 (2007)","journal-title":"Emerg. Artif. Intell. Appl. Comput. Eng."},{"key":"4469_CR26","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273 (1995). https:\/\/doi.org\/10.1007\/BF00994018","journal-title":"Mach. Learn."},{"key":"4469_CR27","doi-asserted-by":"publisher","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees (CRC Press. Boca Raton (1984). https:\/\/doi.org\/10.1201\/9781315139470","DOI":"10.1201\/9781315139470"},{"key":"4469_CR28","doi-asserted-by":"publisher","unstructured":"Schapire, R.E.: The boosting approach to machine learning: an overview, in Nonlinear Estimation and Classification, edited by D.\u00a0D. Denison, M.\u00a0H. Hansen, C.\u00a0C. Holmes, B. Mallick, and B. Yu (Springer New York, New York, NY, 2003) pp. 149\u2013171 https:\/\/doi.org\/10.1007\/978-0-387-21579-2_9","DOI":"10.1007\/978-0-387-21579-2_9"},{"key":"4469_CR29","doi-asserted-by":"crossref","unstructured":"Schapire, R.E.: Explaining adaboost, in Empirical inference (Springer, 2013) pp. 37\u201352","DOI":"10.1007\/978-3-642-41136-6_5"},{"key":"4469_CR30","doi-asserted-by":"publisher","first-page":"429","DOI":"10.3233\/IDA-2002-6504","volume":"6","author":"N Japkowicz","year":"2002","unstructured":"Japkowicz, N., Stephen, S.: The class imbalance problem: a systematic study. Intell. Data Anal. 6, 429 (2002)","journal-title":"Intell. Data Anal."},{"key":"4469_CR31","doi-asserted-by":"crossref","unstructured":"Akbani, R., Kwek, S., Japkowicz, N.: Applying support vector machines to imbalanced datasets, in European conference on machine learning (Springer, 2004) pp. 39\u201350","DOI":"10.1007\/978-3-540-30115-8_7"},{"key":"4469_CR32","doi-asserted-by":"crossref","unstructured":"Tang, Y., Zhang, Y.-Q., Chawla, N.V., Krasser, S.: Svms modeling for highly imbalanced classification, IEEE Transactions on Systems, Man, and Cybernetics. Part B (Cybernetics) 39, 281 (2008)","DOI":"10.1109\/TSMCB.2008.2002909"},{"key":"4469_CR33","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"4469_CR34","doi-asserted-by":"crossref","unstructured":"Subudhi, S., Patro, R.N., Biswal, P.K.: Pso-based synthetic minority oversampling technique for classification of reduced hyperspectral image, in Soft Computing for Problem Solving, edited by J.C. Bansal, K.N. Das, A. Nagar, K. Deep, and A.\u00a0K. Ojha (Springer Singapore, Singapore, 2019) pp. 617\u2013625","DOI":"10.1007\/978-981-13-1592-3_48"},{"key":"4469_CR35","unstructured":"Drummond, C., Holte, R.C, et\u00a0al.: C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling, in Workshop on learning from imbalanced datasets II, Vol. 11 (Citeseer, 2003) pp. 1\u20138"},{"key":"4469_CR36","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1109\/TSMCA.2009.2029559","volume":"40","author":"C Seiffert","year":"2009","unstructured":"Seiffert, C., Khoshgoftaar, T.M., Van Hulse, J., Napolitano, A.: Rusboost: a hybrid approach to alleviating class imbalance. IEEE Trans. Syst. Man Cybern.-Part A: Syst. Hum. 40, 185 (2009)","journal-title":"IEEE Trans. Syst. Man Cybern.-Part A: Syst. Hum."},{"key":"4469_CR37","unstructured":"QMLab: Global collaboration on quamtum machine learning (http:\/\/qmlab.org\/) (2017)"},{"key":"4469_CR38","doi-asserted-by":"publisher","first-page":"3496","DOI":"10.1103\/PhysRevA.60.3496","volume":"60","author":"K Zyczkowski","year":"1999","unstructured":"Zyczkowski, K.: Volume of the set of separable states ii. Phys. Rev. A 60, 3496 (1999). https:\/\/doi.org\/10.1103\/PhysRevA.60.3496","journal-title":"Phys. Rev. A"}],"container-title":["Quantum Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11128-024-04469-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11128-024-04469-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11128-024-04469-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T17:41:13Z","timestamp":1722361273000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11128-024-04469-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,15]]},"references-count":38,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["4469"],"URL":"https:\/\/doi.org\/10.1007\/s11128-024-04469-9","relation":{},"ISSN":["1573-1332"],"issn-type":[{"type":"electronic","value":"1573-1332"}],"subject":[],"published":{"date-parts":[[2024,7,15]]},"assertion":[{"value":"18 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"273"}}