{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T00:58:16Z","timestamp":1776387496874,"version":"3.51.2"},"reference-count":68,"publisher":"Verein zur Forderung des Open Access Publizierens in den Quantenwissenschaften","license":[{"start":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T00:00:00Z","timestamp":1660694400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Marie Sklodowska-Curie","award":["843134"],"award-info":[{"award-number":["843134"]}]},{"name":"European Research Council","award":["740006"],"award-info":[{"award-number":["740006"]}]}],"content-domain":{"domain":["quantum-journal.org"],"crossmark-restriction":false},"short-container-title":["Quantum"],"abstract":"<jats:p>We propose and assess an alternative quantum generator architecture in the context of generative adversarial learning for Monte Carlo event generation, used to simulate particle physics processes at the Large Hadron Collider (LHC). We validate this methodology by implementing the quantum network on artificial data generated from known underlying distributions. The network is then applied to Monte Carlo-generated datasets of specific LHC scattering processes. The new quantum generator architecture leads to a generalization of the state-of-the-art implementations, achieving smaller Kullback-Leibler divergences even with shallow-depth networks. Moreover, the quantum generator successfully learns the underlying distribution functions even if trained with small training sample sets; this is particularly interesting for data augmentation applications. We deploy this novel methodology on two different quantum hardware architectures, trapped-ion and superconducting technologies, to test its hardware-independent viability.<\/jats:p>","DOI":"10.22331\/q-2022-08-17-777","type":"journal-article","created":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T08:24:41Z","timestamp":1660724681000},"page":"777","update-policy":"https:\/\/doi.org\/10.22331\/q-crossmark-policy-page","source":"Crossref","is-referenced-by-count":31,"title":["Style-based quantum generative adversarial networks for Monte Carlo events"],"prefix":"10.22331","volume":"6","author":[{"given":"Carlos","family":"Bravo-Prieto","sequence":"first","affiliation":[{"name":"Quantum Research Centre, Technology Innovation Institute, Abu Dhabi, UAE"},{"name":"Departament de F\u00edsica Qu\u00e0ntica i Astrof\u00edsica and Institut de Ci\u00e8ncies del Cosmos (ICCUB), Universitat de Barcelona, Barcelona, Spain."}]},{"given":"Julien","family":"Baglio","sequence":"additional","affiliation":[{"name":"Theoretical Physics Department, CERN, CH-1211 Geneva 23, Switzerland."}]},{"given":"Marco","family":"C\u00e8","sequence":"additional","affiliation":[{"name":"Theoretical Physics Department, CERN, CH-1211 Geneva 23, Switzerland."}]},{"given":"Anthony","family":"Francis","sequence":"additional","affiliation":[{"name":"Theoretical Physics Department, CERN, CH-1211 Geneva 23, Switzerland."},{"name":"Institute of Physics, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan."}]},{"given":"Dorota M.","family":"Grabowska","sequence":"additional","affiliation":[{"name":"Theoretical Physics Department, CERN, CH-1211 Geneva 23, Switzerland."}]},{"given":"Stefano","family":"Carrazza","sequence":"additional","affiliation":[{"name":"Quantum Research Centre, Technology Innovation Institute, Abu Dhabi, UAE"},{"name":"Theoretical Physics Department, CERN, CH-1211 Geneva 23, Switzerland."},{"name":"TIF Lab, Dipartimento di Fisica, Universit\u00e0 degli Studi di Milano and INFN Sezione di Milano, Milan, Italy."}]}],"member":"9598","published-online":{"date-parts":[[2022,8,17]]},"reference":[{"key":"0","doi-asserted-by":"publisher","unstructured":"J. Preskill, Quantum 2, 79 (2018).","DOI":"10.22331\/q-2018-08-06-79"},{"key":"1","doi-asserted-by":"publisher","unstructured":"F. Arute, K. Arya, R. Babbush, D. Bacon, J. C. Bardin, R. Barends, R. Biswas, S. Boixo, F. G. S. L. Brandao, D. A. Buell, et al., Nature 574, 505 (2019).","DOI":"10.1038\/s41586-019-1666-5"},{"key":"2","doi-asserted-by":"publisher","unstructured":"H.-S. Zhong, H. Wang, Y.-H. Deng, M.-C. Chen, L.-C. Peng, Y.-H. Luo, J. Qin, D. Wu, X. Ding, Y. Hu, et al., Science 370, 1460 (2020).","DOI":"10.1126\/science.abe8770"},{"key":"3","doi-asserted-by":"publisher","unstructured":"M. Cerezo, A. Arrasmith, R. Babbush, S. C. Benjamin, S. Endo, K. Fujii, J. R. McClean, K. Mitarai, X. Yuan, L. Cincio, et al., Nature Reviews Physics 3, 625\u2013644 (2021).","DOI":"10.1038\/s42254-021-00348-9"},{"key":"4","doi-asserted-by":"publisher","unstructured":"K. Bharti, A. Cervera-Lierta, T. H. Kyaw, T. Haug, S. Alperin-Lea, A. Anand, M. Degroote, H. Heimonen, J. S. Kottmann, T. Menke, W.-K. Mok, S. Sim, L.-C. Kwek, and A. Aspuru-Guzik, Reviews of Modern Physics 94, 015004 (2022).","DOI":"10.1103\/RevModPhys.94.015004"},{"key":"5","doi-asserted-by":"publisher","unstructured":"J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, Nature 549, 195 (2017).","DOI":"10.1038\/nature23474"},{"key":"6","doi-asserted-by":"publisher","unstructured":"M. Schuld and F. Petruccione, Supervised learning with quantum computers, Vol. 17 (Springer, 2018).","DOI":"10.1007\/978-3-319-96424-9"},{"key":"7","doi-asserted-by":"publisher","unstructured":"N. Wiebe, D. Braun, and S. Lloyd, Physical Review Letters 109, 050505 (2012).","DOI":"10.1103\/PhysRevLett.109.050505"},{"key":"8","doi-asserted-by":"publisher","unstructured":"S. Lloyd, M. Mohseni, and P. Rebentrost, arXiv preprint arXiv:1307.0411 (2013).","DOI":"10.48550\/arXiv.1307.0411"},{"key":"9","doi-asserted-by":"publisher","unstructured":"P. Rebentrost, M. Mohseni, and S. Lloyd, Physical Review Letters 113, 130503 (2014).","DOI":"10.1103\/physrevlett.113.130503"},{"key":"10","doi-asserted-by":"publisher","unstructured":"I. Kerenidis and A. Prakash, Physical Review A 101, 022316 (2020).","DOI":"10.1103\/PhysRevA.101.022316"},{"key":"11","doi-asserted-by":"publisher","unstructured":"A. W. Harrow, A. Hassidim, and S. Lloyd, Physical Review Letters 103, 150502 (2009).","DOI":"10.1103\/PhysRevLett.103.150502"},{"key":"12","doi-asserted-by":"publisher","unstructured":"M. Benedetti, E. Lloyd, S. Sack, and M. Fiorentini, Quantum Science and Technology 4, 043001 (2019a).","DOI":"10.1088\/2058-9565\/ab4eb5"},{"key":"13","doi-asserted-by":"publisher","unstructured":"S. Sim, P. D. Johnson, and A. Aspuru-Guzik, Advanced Quantum Technologies 2, 1900070 (2019).","DOI":"10.1002\/qute.201900070"},{"key":"14","doi-asserted-by":"publisher","unstructured":"C. Bravo-Prieto, J. Lumbreras-Zarapico, L. Tagliacozzo, and J. I. Latorre, Quantum 4, 272 (2020).","DOI":"10.22331\/q-2020-05-28-272"},{"key":"15","doi-asserted-by":"publisher","unstructured":"M. Larocca, N. Ju, D. Garc\u00eda-Mart\u00edn, P. J. Coles, and M. Cerezo, arXiv preprint arXiv:2109.11676 (2021).","DOI":"10.48550\/arXiv.2109.11676"},{"key":"16","doi-asserted-by":"publisher","unstructured":"M. Schuld, R. Sweke, and J. J. Meyer, Physical Review A 103, 032430 (2021).","DOI":"10.1103\/PhysRevA.103.032430"},{"key":"17","doi-asserted-by":"publisher","unstructured":"T. Goto, Q. H. Tran, and K. Nakajima, Physical Review Letters 127, 090506 (2021).","DOI":"10.1103\/PhysRevLett.127.090506"},{"key":"18","doi-asserted-by":"publisher","unstructured":"A. P\u00e9rez-Salinas, D. L\u00f3pez-N\u00fa\u00f1ez, A. Garc\u00eda-S\u00e1ez, P. Forn-D\u00edaz, and J. I. Latorre, Physical Review A 104, 012405 (2021).","DOI":"10.1103\/PhysRevA.104.012405"},{"key":"19","doi-asserted-by":"publisher","unstructured":"V. Havl\u00ed\u010dek, A. D. C\u00f3rcoles, K. Temme, A. W. Harrow, A. Kandala, J. M. Chow, and J. M. Gambetta, Nature 567, 209 (2019).","DOI":"10.1038\/s41586-019-0980-2"},{"key":"20","doi-asserted-by":"publisher","unstructured":"M. Schuld, A. Bocharov, K. M. Svore, and N. Wiebe, Physical Review A 101, 032308 (2020).","DOI":"10.1103\/physreva.101.032308"},{"key":"21","doi-asserted-by":"publisher","unstructured":"A. P\u00e9rez-Salinas, A. Cervera-Lierta, E. Gil-Fuster, and J. I. Latorre, Quantum 4, 226 (2020).","DOI":"10.22331\/q-2020-02-06-226"},{"key":"22","doi-asserted-by":"publisher","unstructured":"T. Dutta, A. P\u00e9rez-Salinas, J. P. S. Cheng, J. I. Latorre, and M. Mukherjee, Physical Review A 106, 012411 (2022).","DOI":"10.1103\/PhysRevA.106.012411"},{"key":"23","doi-asserted-by":"publisher","unstructured":"J. Romero, J. P. Olson, and A. Aspuru-Guzik, Quantum Science and Technology 2, 045001 (2017).","DOI":"10.1088\/2058-9565\/aa8072"},{"key":"24","doi-asserted-by":"publisher","unstructured":"A. Pepper, N. Tischler, and G. J. Pryde, Physical Review Letters 122, 060501 (2019).","DOI":"10.1103\/PhysRevLett.122.060501"},{"key":"25","doi-asserted-by":"publisher","unstructured":"C. Bravo-Prieto, Machine Learning: Science and Technology 2, 035028 (2021).","DOI":"10.1088\/2632-2153\/ac0616"},{"key":"26","doi-asserted-by":"publisher","unstructured":"C. Cao and X. Wang, Physical Review Applied 15, 054012 (2021).","DOI":"10.1103\/PhysRevApplied.15.054012"},{"key":"27","doi-asserted-by":"publisher","unstructured":"M. Benedetti, D. Garcia-Pintos, O. Perdomo, V. Leyton-Ortega, Y. Nam, and A. Perdomo-Ortiz, npj Quantum Information 5, 1 (2019b).","DOI":"10.1038\/s41534-019-0157-8"},{"key":"28","doi-asserted-by":"publisher","unstructured":"K. E. Hamilton, E. F. Dumitrescu, and R. C. Pooser, Physical Review A 99, 062323 (2019).","DOI":"10.1103\/PhysRevA.99.062323"},{"key":"29","doi-asserted-by":"publisher","unstructured":"B. Coyle, D. Mills, V. Danos, and E. Kashefi, npj Quantum Information 6, 1 (2020).","DOI":"10.1038\/s41534-020-00288-9"},{"key":"30","doi-asserted-by":"publisher","unstructured":"P.-L. Dallaire-Demers and N. Killoran, Physical Review A 98, 012324 (2018).","DOI":"10.1103\/PhysRevA.98.012324"},{"key":"31","doi-asserted-by":"publisher","unstructured":"S. Lloyd and C. Weedbrook, Physical Review Letters 121, 040502 (2018).","DOI":"10.1103\/PhysRevLett.121.040502"},{"key":"32","doi-asserted-by":"publisher","unstructured":"I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, Communications of the ACM 63, 139\u2013144 (2020).","DOI":"10.1145\/3422622"},{"key":"33","doi-asserted-by":"publisher","unstructured":"C. Zoufal, A. Lucchi, and S. Woerner, npj Quantum Information 5, 1 (2019).","DOI":"10.1038\/s41534-019-0223-2"},{"key":"34","doi-asserted-by":"publisher","unstructured":"J. Zeng, Y. Wu, J.-G. Liu, L. Wang, and J. Hu, Physical Review A 99, 052306 (2019).","DOI":"10.1103\/PhysRevA.99.052306"},{"key":"35","doi-asserted-by":"publisher","unstructured":"H. Situ, Z. He, Y. Wang, L. Li, and S. Zheng, Information Sciences 538, 193 (2020).","DOI":"10.1016\/j.ins.2020.05.127"},{"key":"36","doi-asserted-by":"publisher","unstructured":"L. Hu, S.-H. Wu, W. Cai, Y. Ma, X. Mu, Y. Xu, H. Wang, Y. Song, D.-L. Deng, C.-L. Zou, et al., Science advances 5, eaav2761 (2019).","DOI":"10.1126\/sciadv.aav2761"},{"key":"37","doi-asserted-by":"publisher","unstructured":"M. Benedetti, E. Grant, L. Wossnig, and S. Severini, New Journal of Physics 21, 043023 (2019c).","DOI":"10.1088\/1367-2630\/ab14b5"},{"key":"38","doi-asserted-by":"publisher","unstructured":"J. Romero and A. Aspuru-Guzik, Advanced Quantum Technologies 4, 2000003 (2021).","DOI":"10.1002\/qute.202000003"},{"key":"39","doi-asserted-by":"publisher","unstructured":"M. Y. Niu, A. Zlokapa, M. Broughton, S. Boixo, M. Mohseni, V. Smelyanskyi, and H. Neven, Physical Review Letters 128, 220505 (2022).","DOI":"10.1103\/PhysRevLett.128.220505"},{"key":"40","doi-asserted-by":"publisher","unstructured":"T. Karras, S. Laine, and T. Aila, IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 4217 (2021).","DOI":"10.1109\/TPAMI.2020.2970919"},{"key":"41","doi-asserted-by":"publisher","unstructured":"A. P\u00e9rez-Salinas, J. Cruz-Martinez, A. A. Alhajri, and S. Carrazza, Physical Review D 103, 034027 (2021).","DOI":"10.1103\/PhysRevD.103.034027"},{"key":"42","doi-asserted-by":"publisher","unstructured":"W. Guan, G. Perdue, A. Pesah, M. Schuld, K. Terashi, S. Vallecorsa, and J.-R. Vlimant, Machine Learning: Science and Technology 2, 011003 (2021).","DOI":"10.1088\/2632-2153\/abc17d"},{"key":"43","doi-asserted-by":"publisher","unstructured":"S. Y. Chang, S. Vallecorsa, E. F. Combarro, and F. Carminati, arXiv preprint arXiv:2101.11132 (2021a).","DOI":"10.48550\/arXiv.2101.11132"},{"key":"44","doi-asserted-by":"publisher","unstructured":"S. Y. Chang, S. Herbert, S. Vallecorsa, E. F. Combarro, and R. Duncan, EPJ Web of Conferences 251, 03050 (2021b).","DOI":"10.1051\/epjconf\/202125103050"},{"key":"45","doi-asserted-by":"publisher","unstructured":"V. Belis, S. Gonz\u00e1lez-Castillo, C. Reissel, S. Vallecorsa, E. F. Combarro, G. Dissertori, and F. Reiter, EPJ Web of Conferences 251, 03070 (2021).","DOI":"10.1051\/epjconf\/202125103070"},{"key":"46","doi-asserted-by":"publisher","unstructured":"G. R. Khattak, S. Vallecorsa, F. Carminati, and G. M. Khan, The European Physical Journal C 82, 1 (2022).","DOI":"10.1140\/epjc\/s10052-022-10258-4"},{"key":"47","doi-asserted-by":"publisher","unstructured":"P. Baldi, L. Blecher, A. Butter, J. Collado, J. N. Howard, F. Keilbach, T. Plehn, G. Kasieczka, and D. Whiteson, arXiv preprint arXiv:2012.11944 (2021).","DOI":"10.48550\/arXiv.2012.11944"},{"key":"48","doi-asserted-by":"publisher","unstructured":"M. Backes, A. Butter, T. Plehn, and R. Winterhalder, SciPost Physics 10, 89 (2021).","DOI":"10.21468\/SciPostPhys.10.4.089"},{"key":"49","doi-asserted-by":"publisher","unstructured":"A. Butter and T. Plehn, in Artificial Intelligence For High Energy Physics (World Scientific, 2022) pp. 191\u2013240.","DOI":"10.1142\/9789811234033_0007"},{"key":"50","doi-asserted-by":"publisher","unstructured":"A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman, and T. Plehn, SciPost Physics 10, 139 (2021).","DOI":"10.21468\/SciPostPhys.10.6.139"},{"key":"51","doi-asserted-by":"publisher","unstructured":"A. Butter, T. Plehn, and R. Winterhalder, SciPost Physics Core 3, 9 (2020).","DOI":"10.21468\/SciPostPhysCore.3.2.009"},{"key":"52","doi-asserted-by":"publisher","unstructured":"M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, and R. Winterhalder, SciPost Physics 8, 70 (2020).","DOI":"10.21468\/SciPostPhys.8.4.070"},{"key":"53","doi-asserted-by":"publisher","unstructured":"A. Butter, T. Plehn, and R. Winterhalder, SciPost Physics 7, 75 (2019).","DOI":"10.21468\/SciPostPhys.7.6.075"},{"key":"54","doi-asserted-by":"publisher","unstructured":"S. Efthymiou, S. Ramos-Calderer, C. Bravo-Prieto, A. P\u00e9rez-Salinas, D. Garc\u00eda-Mart\u00edn, A. Garcia-Saez, J. I. Latorre, and S. Carrazza, Quantum Science and Technology 7, 015018 (2021a).","DOI":"10.1088\/2058-9565\/ac39f5"},{"key":"55","doi-asserted-by":"publisher","unstructured":"S. Efthymiou, S. Carrazza, S. Ramos, bpcarlos, AdrianPerezSalinas, D. Garc\u00eda-Mart\u00edn, Paul, J. Serrano, and atomicprinter, qiboteam\/qibo: Qibo 0.1.6-rc1 (2021b).","DOI":"10.5281\/zenodo.5088103"},{"key":"56","unstructured":"M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, et al., TensorFlow: Large-scale machine learning on heterogeneous systems (2015), software available from tensorflow.org."},{"key":"57","doi-asserted-by":"publisher","unstructured":"afrancis heplat, C. Bravo-Prieto, S. Carrazza, M. C\u00e8, J. Baglio, and d-m grabowska, Qti-th\/style-qgan: v1.0.0 (2021).","DOI":"10.5281\/zenodo.5567077"},{"key":"58","doi-asserted-by":"publisher","unstructured":"M. D. Zeiler, arXiv preprint arXiv:1212.5701 (2012).","DOI":"10.48550\/arXiv.1212.5701"},{"key":"59","doi-asserted-by":"publisher","unstructured":"M. Ostaszewski, E. Grant, and M. Benedetti, Quantum 5, 391 (2021).","DOI":"10.22331\/q-2021-01-28-391"},{"key":"60","doi-asserted-by":"publisher","unstructured":"S. Kullback and R. A. Leibler, The Annals of Mathematical Statistics 22, 79 (1951).","DOI":"10.1214\/aoms\/1177729694"},{"key":"61","doi-asserted-by":"publisher","unstructured":"M. Frid-Adar, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan, in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (2018) pp. 289\u2013293.","DOI":"10.1109\/ISBI.2018.8363576"},{"key":"62","doi-asserted-by":"publisher","unstructured":"F. H. K. dos Santos Tanaka and C. Aranha, arXiv preprint arXiv:1904.09135 (2019).","DOI":"10.48550\/arXiv.1904.09135"},{"key":"63","doi-asserted-by":"publisher","unstructured":"J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer, H. S. Shao, T. Stelzer, P. Torrielli, and M. Zaro, Journal of High Energy Physics 07, 079 (2014).","DOI":"10.1007\/JHEP07(2014)079"},{"key":"64","doi-asserted-by":"publisher","unstructured":"R. Frederix, S. Frixione, V. Hirschi, D. Pagani, H. S. Shao, and M. Zaro, Journal of High Energy Physics 07, 185 (2018).","DOI":"10.1007\/JHEP07(2018)185"},{"key":"65","doi-asserted-by":"publisher","unstructured":"I.-K. Yeo and R. A. Johnson, Biometrika 87, 954 (2000).","DOI":"10.1093\/biomet\/87.4.954"},{"key":"66","unstructured":"F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, Journal of Machine Learning Research 12, 2825\u20132830 (2011)."},{"key":"67","doi-asserted-by":"publisher","unstructured":"G. Aleksandrowicz, T. Alexander, P. Barkoutsos, L. Bello, Y. Ben-Haim, D. Bucher, F. J. Cabrera-Hern\u00e1ndez, J. Carballo-Franquis, A. Chen, C.-F. Chen, et al., Qiskit: An Open-source Framework for Quantum Computing (2019).","DOI":"10.5281\/zenodo.2562111"}],"container-title":["Quantum"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/quantum-journal.org\/papers\/q-2022-08-17-777\/pdf\/","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T08:24:52Z","timestamp":1660724692000},"score":1,"resource":{"primary":{"URL":"https:\/\/quantum-journal.org\/papers\/q-2022-08-17-777\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,17]]},"references-count":68,"URL":"https:\/\/doi.org\/10.22331\/q-2022-08-17-777","archive":["CLOCKSS"],"relation":{},"ISSN":["2521-327X"],"issn-type":[{"value":"2521-327X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,17]]},"article-number":"777"}}