Comparing quantum and classical machine learning for Vector Boson Scattering background reduction at the Large Hadron Collider

[1]  C. Collaboration,et al.  Evidence for WW/WZ vector boson scattering in the decay channel $\ell\nu$qq produced in association with two jets in proton-proton collisions at $\sqrt{s} =$ 13 TeV , 2021, 2112.05259.

[2]  Jennifer R. Glick,et al.  Application of quantum machine learning using the quantum kernel algorithm on high energy physics analysis at the LHC , 2021, Physical Review Research.

[3]  R. Covarelli,et al.  Vector-Boson scattering at the LHC: Unraveling the electroweak sector , 2021, 2102.10991.

[4]  C. Macchiavello,et al.  Quantum computing models for artificial neural networks , 2021, EPL (Europhysics Letters).

[5]  Miron Livny,et al.  Application of quantum machine learning using the quantum variational classifier method to high energy physics analysis at the LHC on IBM quantum computer simulator and hardware with 10 qubits , 2020, Journal of Physics G: Nuclear and Particle Physics.

[6]  B. Kerševan,et al.  Comparing traditional and deep-learning techniques of kinematic reconstruction for polarization discrimination in vector boson scattering , 2020, The European Physical Journal C.

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

[8]  J. Tanaka,et al.  Event Classification with Quantum Machine Learning in High-Energy Physics , 2020, Computing and Software for Big Science.

[9]  Alessandro Chiesa,et al.  Quantum Computers as Universal Quantum Simulators: State‐of‐the‐Art and Perspectives , 2019, Advanced Quantum Technologies.

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

[11]  Naftali Tishby,et al.  Machine learning and the physical sciences , 2019, Reviews of Modern Physics.

[12]  Yi Wang,et al.  Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at √s=13 TeV , 2018, Journal of Instrumentation.

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

[14]  Abien Fred Agarap.  Deep Learning using Rectified Linear Units (ReLU) , 2018, ArXiv.

[15]  Maria Schuld,et al.  Quantum Machine Learning in Feature Hilbert Spaces. , 2018, Physical review letters.

[16]  Cambridge,et al.  Vector boson scattering: Recent experimental and theory developments , 2018, Reviews in Physics.

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

[18]  C. Collaboration,et al.  Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV , 2017, Journal of Instrumentation.

[19]  A. Ballestrero,et al.  W boson polarization in vector boson scattering at the LHC , 2017, 1710.09339.

[20]  Andrew W. Cross,et al.  Quantum optimization using variational algorithms on near-term quantum devices , 2017, Quantum Science and Technology.

[21]  C. Collaboration,et al.  Particle-flow reconstruction and global event description with the CMS detector , 2017, 1706.04965.

[22]  Scott Lundberg,et al.  A Unified Approach to Interpreting Model Predictions , 2017, NIPS.

[23]  Khachatryan,et al.  Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV , 2016, 1607.03663.

[24]  Cristian Romero García,et al.  Quantum Machine Learning , 2017, Encyclopedia of Machine Learning and Data Mining.

[25]  Jacob biamonte,et al.  Quantum machine learning , 2016, Nature.

[26]  Alán Aspuru-Guzik,et al.  The theory of variational hybrid quantum-classical algorithms , 2015, 1509.04279.

[27]  C. Collaboration,et al.  Performance of electron reconstruction and selection with the CMS detector in proton-proton collisions at √s = 8 TeV , 2015, 1502.02701.

[28]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[29]  Peter Skands,et al.  An introduction to PYTHIA 8.2 , 2014, Comput. Phys. Commun..

[30]  P. Harris,et al.  Pileup per particle identification , 2014, 1407.6013.

[31]  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.

[32]  G. Soyez,et al.  Soft drop , 2014, 1402.2657.

[33]  M. Cacciari,et al.  FastJet user manual , 2011, 1111.6097.

[34]  J. Thaler,et al.  Identifying boosted objects with N-subjettiness , 2010, 1011.2268.

[35]  João Paulo Teixeira,et al.  The CMS experiment at the CERN LHC , 2008 .

[36]  M. Cacciari,et al.  The anti-$k_t$ jet clustering algorithm , 2008, 0802.1189.

[37]  R. Feynman.  Simulating physics with computers , 1999 .

[38]  D. Rainwater,et al.  Probing color-singlet exchange in Z+2-jet events at the CERN LHC. , 1996, Physical review. D, Particles and fields.

[39]  J. Spall.  Multivariate stochastic approximation using a simultaneous perturbation gradient approximation , 1992 .

[40]  L. Shapley.  A Value for n-person Games , 1988 .

[41]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[42]  Performance of the CMS muon detector with 7 TeV pp collisions at LHC , 2011 .

[43]  T. Paul,et al.  Quantum computation and quantum information , 2001, SOEN.

[44]  A. Dell'Acqua,et al.  Geant4—a simulation toolkit , 2003 .

[45]  A. Kleywegt,et al.  Stochastic Optimization , 2003 .