Style-based quantum generative adversarial networks for Monte Carlo events

Carlos Bravo-Prieto, 2 Julien Baglio, Marco Cè, Anthony Francis, 3 Dorota M. Grabowska, and Stefano Carrazza 3, 1 Quantum Research Centre, Technology Innovation Institute, Abu Dhabi, UAE Departament de F́ısica Quàntica i Astrof́ısica and Institut de Ciències del Cosmos (ICCUB), Universitat de Barcelona, Barcelona, Spain. Theoretical Physics Department, CERN, CH-1211 Geneva 23, Switzerland. Institute of Physics, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan. TIF Lab, Dipartimento di Fisica, Università degli Studi di Milano and INFN Sezione di Milano, Milan, Italy.

[1]  A. Butter,et al.  Generative Networks for LHC Events , 2020, Artificial Intelligence for High Energy Physics.

[2]  Geoff J Pryde,et al.  Experimental Realization of a Quantum Autoencoder: The Compression of Qutrits via Machine Learning. , 2018, Physical review letters.

[3]  Maria Schuld,et al.  Supervised Learning with Quantum Computers , 2018 .

[4]  R. Frederix,et al.  The automation of next-to-leading order electroweak calculations , 2018, 1804.10017.

[5]  Tilman Plehn,et al.  How to GAN event subtraction , 2019 .

[6]  Danna Zhou,et al.  d. , 1840, Microbial pathogenesis.

[7]  John Preskill,et al.  Quantum Computing in the NISQ era and beyond , 2018, Quantum.

[8]  G. Kasieczka,et al.  GANplifying event samples , 2020, SciPost Physics.

[9]  Shu-Hao Wu,et al.  Quantum generative adversarial learning in a superconducting quantum circuit , 2018, Science Advances.

[10]  Ievgeniia Oshurko Quantum Machine Learning , 2020, Quantum Computing.

[11]  P. Alam ‘T’ , 2021, Composites Engineering: An A–Z Guide.

[12]  H. Neven,et al.  Entangling Quantum Generative Adversarial Networks. , 2021, Physical review letters.

[13]  S. Carrazza,et al.  Determining the proton content with a quantum computer , 2020, Physical Review D.

[14]  Kristan Temme,et al.  Supervised learning with quantum-enhanced feature spaces , 2018, Nature.

[15]  Simone Severini,et al.  Adversarial quantum circuit learning for pure state approximation , 2018, New Journal of Physics.

[16]  J. Latorre,et al.  Realization of an ion trap quantum classifier , 2021 .

[17]  Gorjan Alagic,et al.  #p , 2019, Quantum information & computation.

[18]  Hayit Greenspan,et al.  Synthetic data augmentation using GAN for improved liver lesion classification , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[19]  Travis S. Humble,et al.  Quantum supremacy using a programmable superconducting processor , 2019, Nature.

[20]  Peter D. Johnson,et al.  Expressibility and Entangling Capability of Parameterized Quantum Circuits for Hybrid Quantum‐Classical Algorithms , 2019, Advanced Quantum Technologies.

[21]  Stefano Carrazza,et al.  Qibo: a framework for quantum simulation with hardware acceleration , 2020, Quantum Science and Technology.

[22]  Gregor Kasieczka,et al.  How to GAN away Detector Effects. , 2019 .

[23]  P. Alam ‘L’ , 2021, Composites Engineering: An A–Z Guide.

[25]  Stefan Woerner,et al.  Quantum Generative Adversarial Networks for learning and loading random distributions , 2019, npj Quantum Information.

[26]  Richard A. Johnson,et al.  A new family of power transformations to improve normality or symmetry , 2000 .

[27]  Carlos Bravo-Prieto,et al.  Quantum autoencoders with enhanced data encoding , 2020, Mach. Learn. Sci. Technol..

[28]  Federico Carminati,et al.  Quantum Generative Adversarial Networks in a Continuous-Variable Architecture to Simulate High Energy Physics Detectors , 2021, ArXiv.

[29]  Ross Duncan,et al.  Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics , 2021, EPJ Web of Conferences.

[30]  Seth Lloyd,et al.  Quantum Generative Adversarial Learning. , 2018, Physical review letters.

[31]  A. Prakash,et al.  Quantum gradient descent for linear systems and least squares , 2017, Physical Review A.

[32]  장윤희,et al.  Y. , 2003, Industrial and Labor Relations Terms.

[33]  Elham Kashefi,et al.  The Born supremacy: quantum advantage and training of an Ising Born machine , 2019, npj Quantum Information.

[34]  Tilman Plehn,et al.  How to GAN LHC events , 2019, SciPost Physics.

[35]  Maria Schuld,et al.  Effect of data encoding on the expressive power of variational quantum-machine-learning models , 2020, Physical Review A.

[36]  Shenggen Zheng,et al.  Quantum generative adversarial network for generating discrete data , 2018 .

[37]  Jos'e I. Latorre,et al.  Data re-uploading for a universal quantum classifier , 2019, Quantum.

[38]  Kohei Nakajima,et al.  Universal Approximation Property of Quantum Machine Learning Models in Quantum-Enhanced Feature Spaces. , 2020, Physical review letters.

[39]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[40]  Jiangping Hu,et al.  Learning and Inference on Generative Adversarial Quantum Circuits , 2018, Physical Review A.

[41]  G. M. Khan,et al.  Fast simulation of a high granularity calorimeter by generative adversarial networks , 2021, The European Physical Journal C.

[42]  Alejandro Perdomo-Ortiz,et al.  A generative modeling approach for benchmarking and training shallow quantum circuits , 2018, npj Quantum Information.

[43]  Arthur Pesah,et al.  Quantum machine learning in high energy physics , 2020, Mach. Learn. Sci. Technol..

[44]  M. Schuld,et al.  Circuit-centric quantum classifiers , 2018, Physical Review A.

[45]  Seth Lloyd,et al.  Quantum algorithm for data fitting. , 2012, Physical review letters.

[46]  Huaiyu Zhu On Information and Sufficiency , 1997 .

[47]  R. Frederix,et al.  The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations , 2014, 1405.0301.

[48]  Xin Wang,et al.  Noise-Assisted Quantum Autoencoder , 2020, 2012.08331.

[49]  Kathleen E. Hamilton,et al.  Generative model benchmarks for superconducting qubits , 2018, Physical Review A.

[50]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[51]  Timo Aila,et al.  A Style-Based Generator Architecture for Generative Adversarial Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[52]  Matthew D. Zeiler ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[53]  Sofia Vallecorsa,et al.  Higgs analysis with quantum classifiers , 2021, EPJ Web of Conferences.

[54]  S. Lloyd,et al.  Quantum algorithms for supervised and unsupervised machine learning , 2013, 1307.0411.

[55]  M. Cerezo,et al.  Variational quantum algorithms , 2020, Nature Reviews Physics.

[56]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[57]  A. Harrow,et al.  Quantum algorithm for linear systems of equations. , 2008, Physical review letters.

[58]  Nathan Killoran,et al.  Quantum generative adversarial networks , 2018, Physical Review A.

[59]  Guangwen Yang,et al.  Quantum computational advantage using photons , 2020, Science.

[60]  Alan Aspuru-Guzik,et al.  Variational Quantum Generators: Generative Adversarial Quantum Machine Learning for Continuous Distributions , 2019, Advanced Quantum Technologies.

[61]  Marcello Benedetti,et al.  Parameterized quantum circuits as machine learning models , 2019, Quantum Science and Technology.

[62]  Alán Aspuru-Guzik,et al.  Quantum autoencoders for efficient compression of quantum data , 2016, 1612.02806.