Built on

B. Hashemi, N. Amin, K. Datta, D. Olivito, and M. Pierini, Lhc analysis-specific datasets with generative adversarial networks (2019), arXiv:1901.05282 [hep-ex]
Original
1901
Earlier work this paper cites.
M. Cacciari, Fastjet: a code for fast k t k_{t} clustering, and more (2006), arXiv:hep-ph/0607071 [hep-ph]
2006
Earlier work this paper cites.
S. Wang, E. Fontana, M. Cerezo, K. Sharma, A. Sone, L. Cincio, and P. J. Coles, Noise-induced barren plateaus in variational quantum algorithms (2020), arXiv:2007.14384 [quant-ph]
Original
2007
Earlier work this paper cites.
T. Sjöstrand, S. Mrenna, and P. Skands, A brief introduction to PYTHIA 8.1, Computer Physics Communications 178
2008
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, The anti-ktjet clustering algorithm, Journal of High Energy Physics 2008
2008
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, Generative adversarial nets, in Advances in Neural Information Processing Systems , Vol. 27, edited by Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Q. Weinberger (Curran Associates, Inc., 2014)
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980 (2014)
Original
2014
Earlier work this paper cites.
J. de Favereau, , C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens, and M. Selvaggi, DELPHES 3: a modular framework for fast simulation of a generic collider experiment, Journal of High Energy Physics 2014
2014
Earlier work this paper cites.
S. Mohamed and B. Lakshminarayanan, Learning in implicit generative models, arXiv preprint arXiv:1610.03483 (2016)
Original
2016
Earlier work this paper cites.
L. de Oliveira, M. Paganini, and B. Nachman, Learning particle physics by example: Location-aware generative adversarial networks for physics synthesis, Computing and Software for Big Science 1
2017
Earlier work this paper cites.

Similar

M. Paganini, L. de Oliveira, and B. Nachman, Calogan: Simulating 3d high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks, Phys. Rev. D 97
2018
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P. Musella and F. Pandolfi, Fast and accurate simulation of particle detectors using generative adversarial networks, Computing and Software for Big Science 2
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J.-G. Liu and L. Wang, Differentiable learning of quantum circuit born machines, Physical Review A 98
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Cited alongside, same era.
M. Benedetti, D. Garcia-Pintos, Y. Nam, and A. Perdomo-Ortiz, A generative modeling approach for benchmarking and training shallow quantum circuits, arXiv preprint arXiv:1801.07686 (2018)
Original
2018
Cited alongside, same era.
J. R. McClean, S. Boixo, V. N. Smelyanskiy, R. Babbush, and H. Neven, Barren plateaus in quantum neural network training landscapes, Nature communications 9

Then

M. Schuld, V. Bergholm, C. Gogolin, J. Izaac, and N. Killoran, Evaluating analytic gradients on quantum hardware, Physical Review A 99
2019
Later among the works it cites.
Y. Du, M.-H. Hsieh, T. Liu, and D. Tao, Expressive power of parametrized quantum circuits, Physical Review Research 2
2020
Later among the works it cites.
V. Bergholm, J. Izaac, M. Schuld, C. Gogolin, M. S. Alam, S. Ahmed, J. M. Arrazola, C. Blank, A. Delgado, S. Jahangiri, K. McKiernan, J. J. Meyer, Z. Niu, A. Száva, and N. Killoran, Pennylane: Automatic differentiation of hybrid quantum-classical computations (2020), arXiv:1811.04968 [quant-ph]
Original
2020
Later among the works it cites.
K. E. Hamilton and R. C. Pooser, Error-mitigated data-driven circuit learning on noisy quantum hardware, Quantum Machine Intelligence 2
2020
Later among the works it cites.

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2022

Unsupervised Quantum Circuit Learning in High Energy Physics

Delgado, Andrea, Hamilton, Kathleen E.

Understand

Unsupervised training of generative models is a machine learning task that has many applications in scientific computing.

In this work we evaluate the efficacy of using quantum circuit-based generative models to generate synthetic data of high energy physics processes.
We use non-adversarial, gradient-based training of quantum circuit Born machines to generate joint distributions over 2 and 3 variables.

Built on

B. Hashemi, N. Amin, K. Datta, D. Olivito, and M. Pierini, Lhc analysis-specific datasets with generative adversarial networks (2019), arXiv:1901.05282 [hep-ex]
Original
1901
Earlier work this paper cites.
M. Cacciari, Fastjet: a code for fast k t k_{t} clustering, and more (2006), arXiv:hep-ph/0607071 [hep-ph]
2006
Earlier work this paper cites.
S. Wang, E. Fontana, M. Cerezo, K. Sharma, A. Sone, L. Cincio, and P. J. Coles, Noise-induced barren plateaus in variational quantum algorithms (2020), arXiv:2007.14384 [quant-ph]
Original
2007
Earlier work this paper cites.
T. Sjöstrand, S. Mrenna, and P. Skands, A brief introduction to PYTHIA 8.1, Computer Physics Communications 178
2008
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, The anti-ktjet clustering algorithm, Journal of High Energy Physics 2008
2008
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, Generative adversarial nets, in Advances in Neural Information Processing Systems , Vol. 27, edited by Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Q. Weinberger (Curran Associates, Inc., 2014)
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980 (2014)
Original
2014
Earlier work this paper cites.
J. de Favereau, , C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens, and M. Selvaggi, DELPHES 3: a modular framework for fast simulation of a generic collider experiment, Journal of High Energy Physics 2014
2014
Earlier work this paper cites.
S. Mohamed and B. Lakshminarayanan, Learning in implicit generative models, arXiv preprint arXiv:1610.03483 (2016)
Original
2016
Earlier work this paper cites.
L. de Oliveira, M. Paganini, and B. Nachman, Learning particle physics by example: Location-aware generative adversarial networks for physics synthesis, Computing and Software for Big Science 1
2017
Earlier work this paper cites.

Similar

M. Paganini, L. de Oliveira, and B. Nachman, Calogan: Simulating 3d high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks, Phys. Rev. D 97
2018
Cited alongside, same era.
P. Musella and F. Pandolfi, Fast and accurate simulation of particle detectors using generative adversarial networks, Computing and Software for Big Science 2
2018
Cited alongside, same era.
J.-G. Liu and L. Wang, Differentiable learning of quantum circuit born machines, Physical Review A 98
2018
Cited alongside, same era.
M. Benedetti, D. Garcia-Pintos, Y. Nam, and A. Perdomo-Ortiz, A generative modeling approach for benchmarking and training shallow quantum circuits, arXiv preprint arXiv:1801.07686 (2018)
Original
2018
Cited alongside, same era.
J. R. McClean, S. Boixo, V. N. Smelyanskiy, R. Babbush, and H. Neven, Barren plateaus in quantum neural network training landscapes, Nature communications 9

Then

M. Schuld, V. Bergholm, C. Gogolin, J. Izaac, and N. Killoran, Evaluating analytic gradients on quantum hardware, Physical Review A 99
2019
Later among the works it cites.
Y. Du, M.-H. Hsieh, T. Liu, and D. Tao, Expressive power of parametrized quantum circuits, Physical Review Research 2
2020
Later among the works it cites.
V. Bergholm, J. Izaac, M. Schuld, C. Gogolin, M. S. Alam, S. Ahmed, J. M. Arrazola, C. Blank, A. Delgado, S. Jahangiri, K. McKiernan, J. J. Meyer, Z. Niu, A. Száva, and N. Killoran, Pennylane: Automatic differentiation of hybrid quantum-classical computations (2020), arXiv:1811.04968 [quant-ph]
Original
2020
Later among the works it cites.
K. E. Hamilton and R. C. Pooser, Error-mitigated data-driven circuit learning on noisy quantum hardware, Quantum Machine Intelligence 2
2020
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

2018

Cited alongside, same era.

A. Butter, T. Plehn, and R. Winterhalder, How to GAN LHC events, SciPost Physics 7

2019

Cited alongside, same era.

R. Di Sipio, M. F. Giannelli, S. K. Haghighat, and S. Palazzo, Dijetgan: a generative-adversarial network approach for the simulation of qcd dijet events at the lhc, Journal of High Energy Physics 2019

2019

Cited alongside, same era.

K. E. Hamilton, E. F. Dumitrescu, and R. C. Pooser, Generative model benchmarks for superconducting qubits, Physical Review A 99

2019

Cited alongside, same era.

S. Sim, P. D. Johnson, and A. Aspuru-Guzik, Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms, Advanced Quantum Technologies 2

2019

Cited alongside, same era.

S. Y. Chang, S. Herbert, S. Vallecorsa, E. F. Combarro, and R. Duncan, Dual-parameterized quantum circuit GAN model in high energy physics, EPJ Web of Conferences 251

Cited in the paper.

S. Y. Chang, S. Vallecorsa, E. F. Combarro, and F. Carminati, Quantum generative adversarial networks in a continuous-variable architecture to simulate high energy physics detectors (2021b), arXiv:2101.11132 [quant-ph]

Original

Cited in the paper.

J. Alwall, M. Herquet, F. Maltoni, O. Mattelaer, and T. Stelzer, MadGraph 5 : Going Beyond, JHEP 06

Original

Cited in the paper.

K. E. Hamilton, T. Kharazi, T. Morris, A. J. McCaskey, R. S. Bennink, and R. C. Pooser, Scalable quantum processor noise characterization, in 2020 IEEE International Conference on Quantum Computing and Engineering (QCE) (IEEE, 2020) pp. 430–440

2020