Style-based quantum generative adversarial networks for Monte Carlo events

Carlos Bravo-Prieto; Julien Baglio; Marco Cè; Anthony Francis; Dorota M. Grabowska; Stefano Carrazza

doi:10.22331/q-2022-08-17-777

Style-based quantum generative adversarial networks for Monte Carlo events

Carlos Bravo-Prieto^1,2, Julien Baglio³, Marco Cè³, Anthony Francis^3,4, Dorota M. Grabowska³, and Stefano Carrazza^1,3,5

¹Quantum Research Centre, Technology Innovation Institute, Abu Dhabi, UAE
²Departament de Física Quàntica i Astrofísica and Institut de Ciències del Cosmos (ICCUB), Universitat de Barcelona, Barcelona, Spain.
³Theoretical Physics Department, CERN, CH-1211 Geneva 23, Switzerland.
⁴Institute of Physics, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.
⁵TIF Lab, Dipartimento di Fisica, Università degli Studi di Milano and INFN Sezione di Milano, Milan, Italy.

Published:	2022-08-17, volume 6, page 777
Eprint:	arXiv:2110.06933v2
Doi:	https://doi.org/10.22331/q-2022-08-17-777
Citation:	Quantum 6, 777 (2022).

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Abstract

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.

Featured image: On the left, schematic steps involved in the style-qGAN training. On the right, marginal samples distributions for the physical observables, s, t, y in pp -> ttbar production at the LHC for the style-qGAN model.

► BibTeX data

@article{BravoPrieto2022stylebasedquantum,
  doi = {10.22331/q-2022-08-17-777},
  url = {https://doi.org/10.22331/q-2022-08-17-777},
  title = {Style-based quantum generative adversarial networks for {M}onte {C}arlo events},
  author = {Bravo-Prieto, Carlos and Baglio, Julien and C{\`{e}}, Marco and Francis, Anthony and Grabowska, Dorota M. and Carrazza, Stefano},
  journal = {{Quantum}},
  issn = {2521-327X},
  publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}},
  volume = {6},
  pages = {777},
  month = aug,
  year = {2022}
}

► References

[1] J. Preskill, Quantum 2, 79 (2018).
https://doi.org/10.22331/q-2018-08-06-79

[2] 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).
https://doi.org/10.1038/s41586-019-1666-5

[3] 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).
https://doi.org/10.1126/science.abe8770

[4] 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, 625–644 (2021).
https://doi.org/10.1038/s42254-021-00348-9

[5] 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).
https://doi.org/10.1103/RevModPhys.94.015004

[6] J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, Nature 549, 195 (2017).
https://doi.org/10.1038/nature23474

[7] M. Schuld and F. Petruccione, Supervised learning with quantum computers, Vol. 17 (Springer, 2018).
https://doi.org/10.1007/978-3-319-96424-9

[8] N. Wiebe, D. Braun, and S. Lloyd, Physical Review Letters 109, 050505 (2012).
https://doi.org/10.1103/PhysRevLett.109.050505

[9] S. Lloyd, M. Mohseni, and P. Rebentrost, arXiv preprint arXiv:1307.0411 (2013).
https://doi.org/10.48550/arXiv.1307.0411
arXiv:1307.0411

[10] P. Rebentrost, M. Mohseni, and S. Lloyd, Physical Review Letters 113, 130503 (2014).
https://doi.org/10.1103/physrevlett.113.130503

[11] I. Kerenidis and A. Prakash, Physical Review A 101, 022316 (2020).
https://doi.org/10.1103/PhysRevA.101.022316

[12] A. W. Harrow, A. Hassidim, and S. Lloyd, Physical Review Letters 103, 150502 (2009).
https://doi.org/10.1103/PhysRevLett.103.150502

[13] M. Benedetti, E. Lloyd, S. Sack, and M. Fiorentini, Quantum Science and Technology 4, 043001 (2019a).
https://doi.org/10.1088/2058-9565/ab4eb5

[14] S. Sim, P. D. Johnson, and A. Aspuru-Guzik, Advanced Quantum Technologies 2, 1900070 (2019).
https://doi.org/10.1002/qute.201900070

[15] C. Bravo-Prieto, J. Lumbreras-Zarapico, L. Tagliacozzo, and J. I. Latorre, Quantum 4, 272 (2020).
https://doi.org/10.22331/q-2020-05-28-272

[16] M. Larocca, N. Ju, D. García-Martín, P. J. Coles, and M. Cerezo, arXiv preprint arXiv:2109.11676 (2021).
https://doi.org/10.48550/arXiv.2109.11676
arXiv:2109.11676

[17] M. Schuld, R. Sweke, and J. J. Meyer, Physical Review A 103, 032430 (2021).
https://doi.org/10.1103/PhysRevA.103.032430

[18] T. Goto, Q. H. Tran, and K. Nakajima, Physical Review Letters 127, 090506 (2021).
https://doi.org/10.1103/PhysRevLett.127.090506

[19] A. Pérez-Salinas, D. López-Núñez, A. García-Sáez, P. Forn-Díaz, and J. I. Latorre, Physical Review A 104, 012405 (2021).
https://doi.org/10.1103/PhysRevA.104.012405

[20] V. Havlíček, A. D. Córcoles, K. Temme, A. W. Harrow, A. Kandala, J. M. Chow, and J. M. Gambetta, Nature 567, 209 (2019).
https://doi.org/10.1038/s41586-019-0980-2

[21] M. Schuld, A. Bocharov, K. M. Svore, and N. Wiebe, Physical Review A 101, 032308 (2020).
https://doi.org/10.1103/physreva.101.032308

[22] A. Pérez-Salinas, A. Cervera-Lierta, E. Gil-Fuster, and J. I. Latorre, Quantum 4, 226 (2020).
https://doi.org/10.22331/q-2020-02-06-226

[23] T. Dutta, A. Pérez-Salinas, J. P. S. Cheng, J. I. Latorre, and M. Mukherjee, Physical Review A 106, 012411 (2022).
https://doi.org/10.1103/PhysRevA.106.012411

[24] J. Romero, J. P. Olson, and A. Aspuru-Guzik, Quantum Science and Technology 2, 045001 (2017).
https://doi.org/10.1088/2058-9565/aa8072

[25] A. Pepper, N. Tischler, and G. J. Pryde, Physical Review Letters 122, 060501 (2019).
https://doi.org/10.1103/PhysRevLett.122.060501

[26] C. Bravo-Prieto, Machine Learning: Science and Technology 2, 035028 (2021).
https://doi.org/10.1088/2632-2153/ac0616

[27] C. Cao and X. Wang, Physical Review Applied 15, 054012 (2021).
https://doi.org/10.1103/PhysRevApplied.15.054012

[28] M. Benedetti, D. Garcia-Pintos, O. Perdomo, V. Leyton-Ortega, Y. Nam, and A. Perdomo-Ortiz, npj Quantum Information 5, 1 (2019b).
https://doi.org/10.1038/s41534-019-0157-8

[29] K. E. Hamilton, E. F. Dumitrescu, and R. C. Pooser, Physical Review A 99, 062323 (2019).
https://doi.org/10.1103/PhysRevA.99.062323

[30] B. Coyle, D. Mills, V. Danos, and E. Kashefi, npj Quantum Information 6, 1 (2020).
https://doi.org/10.1038/s41534-020-00288-9

[31] P.-L. Dallaire-Demers and N. Killoran, Physical Review A 98, 012324 (2018).
https://doi.org/10.1103/PhysRevA.98.012324

[32] S. Lloyd and C. Weedbrook, Physical Review Letters 121, 040502 (2018).
https://doi.org/10.1103/PhysRevLett.121.040502

[33] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, Communications of the ACM 63, 139–144 (2020).
https://doi.org/10.1145/3422622

[34] C. Zoufal, A. Lucchi, and S. Woerner, npj Quantum Information 5, 1 (2019).
https://doi.org/10.1038/s41534-019-0223-2

[35] J. Zeng, Y. Wu, J.-G. Liu, L. Wang, and J. Hu, Physical Review A 99, 052306 (2019).
https://doi.org/10.1103/PhysRevA.99.052306

[36] H. Situ, Z. He, Y. Wang, L. Li, and S. Zheng, Information Sciences 538, 193 (2020).
https://doi.org/10.1016/j.ins.2020.05.127

[37] 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).
https://doi.org/10.1126/sciadv.aav2761

[38] M. Benedetti, E. Grant, L. Wossnig, and S. Severini, New Journal of Physics 21, 043023 (2019c).
https://doi.org/10.1088/1367-2630/ab14b5

[39] J. Romero and A. Aspuru-Guzik, Advanced Quantum Technologies 4, 2000003 (2021).
https://doi.org/10.1002/qute.202000003

[40] M. Y. Niu, A. Zlokapa, M. Broughton, S. Boixo, M. Mohseni, V. Smelyanskyi, and H. Neven, Physical Review Letters 128, 220505 (2022).
https://doi.org/10.1103/PhysRevLett.128.220505

[41] T. Karras, S. Laine, and T. Aila, IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 4217 (2021).
https://doi.org/10.1109/TPAMI.2020.2970919

[42] A. Pérez-Salinas, J. Cruz-Martinez, A. A. Alhajri, and S. Carrazza, Physical Review D 103, 034027 (2021).
https://doi.org/10.1103/PhysRevD.103.034027

[43] W. Guan, G. Perdue, A. Pesah, M. Schuld, K. Terashi, S. Vallecorsa, and J.-R. Vlimant, Machine Learning: Science and Technology 2, 011003 (2021).
https://doi.org/10.1088/2632-2153/abc17d

[44] S. Y. Chang, S. Vallecorsa, E. F. Combarro, and F. Carminati, arXiv preprint arXiv:2101.11132 (2021a).
https://doi.org/10.48550/arXiv.2101.11132
arXiv:2101.11132

[45] S. Y. Chang, S. Herbert, S. Vallecorsa, E. F. Combarro, and R. Duncan, EPJ Web of Conferences 251, 03050 (2021b).
https://doi.org/10.1051/epjconf/202125103050

[46] V. Belis, S. González-Castillo, C. Reissel, S. Vallecorsa, E. F. Combarro, G. Dissertori, and F. Reiter, EPJ Web of Conferences 251, 03070 (2021).
https://doi.org/10.1051/epjconf/202125103070

[47] G. R. Khattak, S. Vallecorsa, F. Carminati, and G. M. Khan, The European Physical Journal C 82, 1 (2022).
https://doi.org/10.1140/epjc/s10052-022-10258-4

[48] 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).
https://doi.org/10.48550/arXiv.2012.11944
arXiv:2012.11944

[49] M. Backes, A. Butter, T. Plehn, and R. Winterhalder, SciPost Physics 10, 89 (2021).
https://doi.org/10.21468/SciPostPhys.10.4.089

[50] A. Butter and T. Plehn, in Artificial Intelligence For High Energy Physics (World Scientific, 2022) pp. 191–240.
https://doi.org/10.1142/9789811234033_0007

[51] A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman, and T. Plehn, SciPost Physics 10, 139 (2021).
https://doi.org/10.21468/SciPostPhys.10.6.139

[52] A. Butter, T. Plehn, and R. Winterhalder, SciPost Physics Core 3, 9 (2020).
https://doi.org/10.21468/SciPostPhysCore.3.2.009

[53] M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, and R. Winterhalder, SciPost Physics 8, 70 (2020).
https://doi.org/10.21468/SciPostPhys.8.4.070

[54] A. Butter, T. Plehn, and R. Winterhalder, SciPost Physics 7, 75 (2019).
https://doi.org/10.21468/SciPostPhys.7.6.075

[55] S. Efthymiou, S. Ramos-Calderer, C. Bravo-Prieto, A. Pérez-Salinas, D. García-Martín, A. Garcia-Saez, J. I. Latorre, and S. Carrazza, Quantum Science and Technology 7, 015018 (2021a).
https://doi.org/10.1088/2058-9565/ac39f5

[56] S. Efthymiou, S. Carrazza, S. Ramos, bpcarlos, AdrianPerezSalinas, D. García-Martín, Paul, J. Serrano, and atomicprinter, qiboteam/qibo: Qibo 0.1.6-rc1 (2021b).
https://doi.org/10.5281/zenodo.5088103

[57] 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.
https://www.tensorflow.org/

[58] afrancis heplat, C. Bravo-Prieto, S. Carrazza, M. Cè, J. Baglio, and d-m grabowska, Qti-th/style-qgan: v1.0.0 (2021).
https://doi.org/10.5281/zenodo.5567077

[59] M. D. Zeiler, arXiv preprint arXiv:1212.5701 (2012).
https://doi.org/10.48550/arXiv.1212.5701
arXiv:1212.5701

[60] M. Ostaszewski, E. Grant, and M. Benedetti, Quantum 5, 391 (2021).
https://doi.org/10.22331/q-2021-01-28-391

[61] S. Kullback and R. A. Leibler, The Annals of Mathematical Statistics 22, 79 (1951).
https://doi.org/10.1214/aoms/1177729694

[62] 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. 289–293.
https://doi.org/10.1109/ISBI.2018.8363576

[63] F. H. K. dos Santos Tanaka and C. Aranha, arXiv preprint arXiv:1904.09135 (2019).
https://doi.org/10.48550/arXiv.1904.09135
arXiv:1904.09135

[64] 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).
https://doi.org/10.1007/JHEP07(2014)079

[65] R. Frederix, S. Frixione, V. Hirschi, D. Pagani, H. S. Shao, and M. Zaro, Journal of High Energy Physics 07, 185 (2018).
https://doi.org/10.1007/JHEP07(2018)185

[66] I.-K. Yeo and R. A. Johnson, Biometrika 87, 954 (2000).
https://doi.org/10.1093/biomet/87.4.954

[67] 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, 2825–2830 (2011).
https://dl.acm.org/doi/10.5555/1953048.2078195

[68] G. Aleksandrowicz, T. Alexander, P. Barkoutsos, L. Bello, Y. Ben-Haim, D. Bucher, F. J. Cabrera-Hernández, J. Carballo-Franquis, A. Chen, C.-F. Chen, et al., Qiskit: An Open-source Framework for Quantum Computing (2019).
https://doi.org/10.5281/zenodo.2562111

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[11] Gabriele Agliardi, Michele Grossi, Mathieu Pellen, and Enrico Prati, "Quantum integration of elementary particle processes", Physics Letters B 832, 137228 (2022).

[12] Carlos A. Riofrío, Oliver Mitevski, Caitlin Jones, Florian Krellner, Aleksandar Vučković, Joseph Doetsch, Johannes Klepsch, Thomas Ehmer, and Andre Luckow, "A performance characterization of quantum generative models", arXiv:2301.09363, (2023).

[13] S. Carrazza, S. Efthymiou, M. Lazzarin, and A. Pasquale, "An open-source modular framework for quantum computing", Journal of Physics Conference Series 2438 1, 012148 (2023).

[14] Sandra Nguemto and Vicente Leyton-Ortega, "Re-QGAN: an optimized adversarial quantum circuit learning framework", arXiv:2208.02165, (2022).

[15] Herschel A. Chawdhry and Mathieu Pellen, "Quantum simulation of colour in perturbative quantum chromodynamics", arXiv:2303.04818, (2023).

[16] Armand Rousselot and Michael Spannowsky, "Generative Invertible Quantum Neural Networks", arXiv:2302.12906, (2023).

[17] Benjamin Nachman and Ramon Winterhalder, "ELSA -- Enhanced latent spaces for improved collider simulations", arXiv:2305.07696, (2023).

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