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Machine learning has been used in high energy physics for a long time, primarily at the analysis level with supervised classification.
Simulating physics with computers
Richard P. Feynman · 1982
Earlier work this paper cites.
Optimization by simulated annealing
S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi · 1983
Earlier work this paper cites.
Quantum theory, the church–turing principle and the universal quantum computer
David Deutsch · 1985
Earlier work this paper cites.
A training algorithm for optimal margin classifiers
Bernhard E. Boser, Isabelle M. Guyon, and Vladimir N. Vapnik · 1992
Earlier work this paper cites.
A direct search optimization method that models the objective and constraint functions by linear interpolation
M. J. D. Powell · 1994
Earlier work this paper cites.
A fast quantum mechanical algorithm for database search
Lov K. Grover · 1996
Earlier work this paper cites.
Universal quantum simulators
Seth Lloyd · 1996
Earlier work this paper cites.
Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer
Peter W. Shor · 1997
Earlier work this paper cites.
A one-measurement form of simultaneous perturbation stochastic approximation
James C.Spall · 1997
Earlier work this paper cites.
Quantum annealing in the transverse ising model
Tadashi Kadowaki and Hidetoshi Nishimori · 1998
Earlier work this paper cites.
Quantum computation by adiabatic evolution, 2000
Edward Farhi, Jeffrey Goldstone, Sam Gutmann, and Michael Sipser · 2000
Earlier work this paper cites.
Adaptive stochastic approximation by the simultaneous perturbation method
James C.Spall · 2000
Earlier work this paper cites.
Quantum search by local adiabatic evolution
Jérémie Roland and Nicolas J. Cerf · 2002
Earlier work this paper cites.
Training products of experts by minimizing contrastive divergence
Geoffrey E. Hinton · 2002
Earlier work this paper cites.
Feedback-optimized parallel tempering monte carlo
Helmut G Katzgraber, Simon Trebst, David A Huse, and Matthias Troyer · 2006
Earlier work this paper cites.
Training a binary classifier with the quantum adiabatic algorithm
Hartmut Neven, Vasil S Denchev, Geordie Rose, and William G Macready · 2008
Earlier work this paper cites.
Quantum algorithm for linear systems of equations
Aram W. Harrow, Avinatan Hassidim, and Seth Lloyd · 2009
Earlier work this paper cites.
Nips 2009 demonstration: Binary classification using hardware implementation of quantum annealing
Harmut Neven, Vasil S Denchev, Marshall Drew-Brook, Jiayong Zhang, William G Macready, and Geordie Rose · 2009
Earlier work this paper cites.
Observation of a new boson at a mass of 125 gev with the cms experiment at the lhc
S. Chatrchyan et al · 2012
Earlier work this paper cites.
Observation of a new particle in the search for the standard model higgs boson with the atlas detector at the lhc
G. Aad et al · 2012
Earlier work this paper cites.
Quantum adiabatic machine learning
Kristen L. Pudenz and Daniel A. Lidar · 2013
Earlier work this paper cites.
A quantum approximate optimization algorithm, 2014
Edward Farhi, Jeffrey Goldstone, and Sam Gutmann · 2014
Earlier work this paper cites.
Quantum Machine Learning: What Quantum Computing Means to Data Mining
Peter Wittek · 2014
Earlier work this paper cites.
Quantum principal component analysis
Seth Lloyd, Masoud Mohseni, and Patrick Rebentrost · 2014
Earlier work this paper cites.
Quantum support vector machine for big data classification
Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd · 2014
Earlier work this paper cites.
Quantum simulation
I. M. Georgescu, S. Ashhab, and Franco Nori · 2014
Earlier work this paper cites.
Application of quantum annealing to training of deep neural networks, 2015
Steven H. Adachi and Maxwell P. Henderson · 2015
Earlier work this paper cites.
Application of quantum annealing to training of deep neural networks
Steven H. Adachi and Maxwell P. Henderson · 2015
Earlier work this paper cites.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
Earlier work this paper cites.
Read the fine print
Scott Aaronson · 2015
Earlier work this paper cites.
Quantum learning of coherent states
Gael Sentís, Mădălin Guţă, and Gerardo Adesso · 2015
Earlier work this paper cites.
Real-time dynamics of lattice gauge theories with a few-qubit quantum computer
Esteban A Martinez, Christine A Muschik, Philipp Schindler, Daniel Nigg, Alexander Erhard, Markus Heyl, Philipp Hauke, Marcello Dalmonte, Thomas Monz, Peter Zoller, et al · 2016
Earlier work this paper cites.
The theory of variational hybrid quantum-classical algorithms
Jarrod R McClean, Jonathan Romero, Ryan Babbush, and Alán Aspuru-Guzik · 2016
Earlier work this paper cites.
Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning
Marcello Benedetti, John Realpe-Gómez, Rupak Biswas, and Alejandro Perdomo-Ortiz · 2016
Earlier work this paper cites.
Quantum speed-ups for solving semidefinite programs
F. G. S. L. Brandao and K. M. Svore · 2017
Earlier work this paper cites.
Quantum machine learning
Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd · 2017
Earlier work this paper cites.
Quantum machine learning
Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd · 2017
Earlier work this paper cites.
Guest column: A survey of quantum learning theory
Srinivasan Arunachalam and Ronald de Wolf · 2017
Earlier work this paper cites.
Quantum recommendation systems
Iordanis Kerenedis and Anupam Prakash · 2017
Cited alongside, same era.
Quantum-assisted learning of hardware-embedded probabilistic graphical models
Marcello Benedetti, John Realpe-Gómez, Rupak Biswas, and Alejandro Perdomo-Ortiz · 2017
Cited alongside, same era.
Solving a higgs optimization problem with quantum annealing for machine learning
Alex Mott, Joshua Job, Jean-Roch Vlimant, Daniel Lidar, and Maria Spiropulu · 2017
Cited alongside, same era.
High luminosity large hadron collider hl-lhc, 2017
T. Nakamoto L. Rossi G. Apollinari, O. Bruening · 2017
Cited alongside, same era.
ACTS: from ATLAS software towards a common track reconstruction software
C Gumpert, A Salzburger, M Kiehn, J Hrdinka, and N Calace and · 2017
Cited alongside, same era.
The hep.trkx project: deep neural networks for hl-lhc online and offline tracking
Farrell, Steven, Anderson, Dustin, Calafiura, Paolo, Cerati, Giuseppe, Gray, Lindsey, Kowalkowski, Jim, Mudigonda, Mayur, Prabhat, Spentzouris, Panagiotis, Spiropoulou, Maria, Tsaris, Aristeidis, Vlimant, Jean-Roch, and Zheng, Stephan · 2017
A pattern recognition algorithm for quantum annealers
Frederic Bapst, Wahid Bhimji, Paolo Calafiura, Heather Gray, Wim Lavrijsen, and Lucy Linder · 2019
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A quantum algorithm for high energy physics simulations
Christian W. Bauer, Wibe A. De Jong, Benjamin Nachman, and Davide Provasoli · 2019
Later among the works it cites.
Charged Particle Tracking with Quantum Annealing-Inspired Optimization
Alexander Zlokapa, Abhishek Anand, Jean-Roch Vlimant, Javier M. Duarte, Joshua Job, Daniel Lidar, and Maria Spiropulu · 2019
Later among the works it cites.
Unfolding measurement distributions via quantum annealing
Kyle Cormier, Riccardo Di Sipio, and Peter Wittek · 2019
Later among the works it cites.
Quantum chemistry in the age of quantum computing
Yudong Cao, Jonathan Romero, Jonathan P. Olson, Matthias Degroote, Peter D. Johnson, Mária Kieferová, Ian D. Kivlichan, Tim Menke, Borja Peropadre, Nicolas P. D. Sawaya, Sukin Sim, Libor Veis, and Alán Aspuru-Guzik · 2019
Later among the works it cites.
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Cited alongside, same era.
UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
Cited alongside, same era.
Partitioning optimization problems for hybrid classcal/quantum execution, 2017
M Booth, SP Reinhardt, and A Roy · 2017
Cited alongside, same era.
Implementing a distance-based classifier with a quantum interference circuit
M Schuld, M Fingerhuth, and F Petruccione · 2017
Cited alongside, same era.
Error mitigation for short-depth quantum circuits
Kristan Temme, Sergey Bravyi, and Jay M Gambetta · 2017
Cited alongside, same era.
Binary classification of quantum states: Supervised and unsupervised learning
Farzad Shahi and Ali T Rezakhani · 2017
Cited alongside, same era.
Machine learning at the energy and intensity frontiers of particle physics
Alexander Radovic, Mike Williams, David Rousseau, Michael Kagan, Daniele Bonacorsi, Alexander Himmel, Adam Aurisano, Kazuhiro Terao, and Taritree Wongjirad · 2018
Cited alongside, same era.
Alessandro Roggero and Joseph Carlson · 2019
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Simulations of subatomic many-body physics on a quantum frequency processor
Hsuan-Hao Lu, Natalie Klco, Joseph M Lukens, Titus D Morris, Aaina Bansal, Andreas Ekström, Gaute Hagen, Thomas Papenbrock, Andrew M Weiner, Martin J Savage, et al · 2019
Later among the works it cites.
Challenges and opportunities of near-term quantum computing systems
Antonio D Córcoles, Abhinav Kandala, Ali Javadi-Abhari, Douglas T McClure, Andrew W Cross, Kristan Temme, Paul D Nation, Matthias Steffen, and JM Gambetta · 2019
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Classical algorithms for quantum mean values, 2019
Sergey Bravyi, David Gosset, and Ramis Movassagh · 2019
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Evaluating analytic gradients on quantum hardware
Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, and Nathan Killoran · 2019
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Aram Harrow and John Napp · 2019
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A generative modeling approach for benchmarking and training shallow quantum circuits
Marcello Benedetti, Delfina Garcia-Pintos, Oscar Perdomo, Vicente Leyton-Ortega, Yunseong Nam, and Alejandro Perdomo-Ortiz · 2019
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Quantum adiabatic machine learning with zooming, 2019
Alexander Zlokapa, Alex Mott, Joshua Job, Jean-Roch Vlimant, Daniel Lidar, and Maria Spiropulu · 2019
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Restricted Boltzmann Machines for galaxy morphology classification with a quantum annealer
João Caldeira, Joshua Job, Steven H. Adachi, Brian Nord, and Gabriel N. Perdue · 2019
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Application of quantum machine learning to high energy physics analysis at lhc using ibm quantum computer simulators and ibm quantum computer hardware
Jay Chan, Wen Guan, Shaojun Sun, Alex Zeng Wang, Sau Lan Wu, Chen Zhou, Miron Livny, Federico Carminati, and Alberto Di Meglio · 2019
Later among the works it cites.
Technical Report ATL-PHYS-PUB-2019-041, CERN, Geneva, Oct 2019
Fast Track Reconstruction for HL-LHC · 2019
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Kalman filter track reconstruction on fpgas for acceleration of the high level trigger of the cms experiment at the hl-lhc
Summers, Sioni and Rose, Andrew · 2019
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The tracking machine learning challenge: Accuracy phase
Sabrina Amrouche, Laurent Basara, Paolo Calafiura, Victor Estrade, Steven Farrell, Diogo R. Ferreira, Liam Finnie, Nicole Finnie, Cécile Germain, Vladimir Vava Gligorov, and et al · 2019
Later among the works it cites.
Supervised learning with quantum-enhanced feature spaces
Vojtech Havlícek, Antonio D. Córcoles, Kristan Temme, Aram W. Harrow, Abhinav Kandala, Jerry M. Chow, and Jay M. Gambetta · 2019
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A quantum-inspired classical algorithm for recommendation systems
Ewin Tang · 2019
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Quantum-inspired algorithms in practice
Juan Miguel Arrazola, Alain Delgado, Bhaskar Roy Bardhan, and Seth Lloyd · 2019
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Supervised learning with quantum-enhanced feature spaces
Vojtěch Havlíček, Antonio D Córcoles, Kristan Temme, Aram W Harrow, Abhinav Kandala, Jerry M Chow, and Jay M Gambetta · 2019
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Automated quantum programming via reinforcement learning for combinatorial optimization, 2019
Keri A. McKiernan, Erik Davis, M. Sohaib Alam, and Chad Rigetti · 2019
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Evaluating analytic gradients on quantum hardware
Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, and Nathan Killoran · 2019
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Unsupervised classification of quantum data
Gael Sentís, Alex Monràs, Ramon Muñoz-Tapia, John Calsamiglia, and Emilio Bagan · 2019
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Quantum convolutional neural networks
Iris Cong, Soonwon Choi, and Mikhail D Lukin · 2019
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Putting the squeeze on axions
Karl van Bibber, Konrad Lehnert, and Aaron Chou · 2019
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Quantum algorithms and lower bounds for convex optimization
Shouvanik Chakrabarti, Andrew M. Childs, Tongyang Li, and Xiaodi Wu · 2020
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Quantum computing for neutrino-nucleus scattering
Alessandro Roggero, Andy C. Y. Li, Joseph Carlson, Rajan Gupta, and Gabriel N. Perdue · 2020
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Su (2) non-abelian gauge field theory in one dimension on digital quantum computers
Natalie Klco, Martin J Savage, and Jesse R Stryker · 2020
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Fast quantum learning with statistical guarantees
Carlo Ciliberto, Andrea Rocchetto, Alessandro Rudi, and Leonard Wossnig · 2020
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Quantum embeddings for machine learning
Seth Lloyd, Maria Schuld, Aroosa Ijaz, Josh Izaac, and Nathan Killoran · 2020
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Tensorflow quantum: A software framework for quantum machine learning
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Particle track reconstruction with quantum algorithms, 2020
B. Dermikoz D. Dobos F. Fracas K. Novotny K. Potamianos S. Vallecorsa J. Vlimant C. Tuysuz, F. Carminati · 2020
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Event classification with quantum machine learning in high-energy physics, 2020
Koji Terashi, Michiru Kaneda, Tomoe Kishimoto, Masahiko Saito, Ryu Sawada, and Junichi Tanaka · 2020
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Digital annealer, 2020
Fujitsu · 2020
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No free lunch for quantum machine learning
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Variational quantum fidelity estimation
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