Quantum machine learning in high energy physics

Machine learning has been used in high energy physics (HEP) for a long time, primarily at the analysis level with supervised classification. Quantum computing was postulated in the early 1980s as way to perform computations that would not be tractable with a classical computer. With the advent of noisy intermediate-scale quantum computing devices, more quantum algorithms are being developed with the aim at exploiting the capacity of the hardware for machine learning applications. An interesting question is whether there are ways to apply quantum machine learning to HEP. This paper reviews the first generation of ideas that use quantum machine learning on problems in HEP and provide an outlook on future applications.

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

[2]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[3]  D. Deutsch.  Quantum theory, the Church–Turing principle and the universal quantum computer , 1985, Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences.

[4]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[5]  M. Powell.  A Direct Search Optimization Method That Models the Objective and Constraint Functions by Linear Interpolation , 1994 .

[6]  Lov K. Grover.  A fast quantum mechanical algorithm for database search , 1996, STOC '96.

[7]  Seth Lloyd,et al.  Universal Quantum Simulators , 1996, Science.

[8]  James C. Spall,et al.  A one-measurement form of simultaneous perturbation stochastic approximation , 1997, Autom..

[9]  H. Nishimori,et al.  Quantum annealing in the transverse Ising model , 1998, cond-mat/9804280.

[10]  J. Spall.  Adaptive stochastic approximation by the simultaneous perturbation method , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[11]  Peter W. Shor,et al.  Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer , 1995, SIAM Rev..

[12]  M. Sipser,et al.  Quantum Computation by Adiabatic Evolution , 2000, quant-ph/0001106.

[13]  James C. Spall,et al.  Adaptive stochastic approximation by the simultaneous perturbation method , 2000, IEEE Trans. Autom. Control..

[14]  N. Cerf,et al.  Quantum search by local adiabatic evolution , 2001, quant-ph/0107015.

[15]  Geoffrey E. Hinton.  Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[16]  今井 浩.  20世紀の名著名論：Peter Shor : Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer , 2004 .

[17]  Matthias Troyer,et al.  Feedback-optimized parallel tempering Monte Carlo , 2006, cond-mat/0602085.

[18]  Vasil S. Denchev,et al.  Training a Binary Classifier with the Quantum Adiabatic Algorithm , 2008, 0811.0416.

[19]  Hartmut Neven,et al.  NIPS 2009 Demonstration: Binary Classification using Hardware Implementation of Quantum Annealing , 2009 .

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

[21]  A. Trzupek,et al.  Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC , 2012 .

[22]  Ryszard S. Romaniuk,et al.  Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC , 2012 .

[23]  Daniel A. Lidar,et al.  Quantum adiabatic machine learning , 2011, Quantum Inf. Process..

[24]  F. Nori,et al.  Quantum Simulation , 2013, Quantum Atom Optics.

[25]  Gerardo Adesso,et al.  Quantum learning of coherent states , 2014, 1410.8700.

[26]  Masoud Mohseni,et al.  Quantum support vector machine for big feature and big data classification , 2013, Physical review letters.

[27]  Peter Wittek,et al.  Quantum Machine Learning: What Quantum Computing Means to Data Mining , 2014 .

[28]  E. Farhi,et al.  A Quantum Approximate Optimization Algorithm , 2014, 1411.4028.

[29]  S. Lloyd,et al.  Quantum principal component analysis , 2013, Nature Physics.

[30]  Ryan Babbush,et al.  The theory of variational hybrid quantum-classical algorithms , 2015, 1509.04279.

[31]  Steven H. Adachi,et al.  Application of Quantum Annealing to Training of Deep Neural Networks , 2015, ArXiv.

[32]  S. Aaronson.  Read the fine print , 2015, Nature Physics.

[33]  L. Rossi,et al.  Chapter 1: High Luminosity Large Hadron Collider HL-LHC , 2016, 1705.08830.

[34]  T. Monz,et al.  Real-time dynamics of lattice gauge theories with a few-qubit quantum computer , 2016, Nature.

[35]  M. Benedetti,et al.  Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning , 2015, 1510.07611.

[36]  Roger Melko,et al.  Quantum Boltzmann Machine , 2016, 1601.02036.

[37]  Rupak Biswas,et al.  Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models , 2016, 1609.02542.

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

[39]  A. Rezakhani,et al.  Binary classification of quantum states: Supervised and unsupervised learning , 2017, 1704.01965.

[40]  Steven P. Reinhardt,et al.  Partitioning Optimization Problems for Hybrid Classical/Quantum Execution TECHNICAL REPORT , 2017 .

[41]  Kristan Temme,et al.  Error Mitigation for Short-Depth Quantum Circuits. , 2016, Physical review letters.

[42]  Prabhat,et al.  The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking , 2017 .

[43]  Ronald de Wolf,et al.  Guest Column: A Survey of Quantum Learning Theory , 2017, SIGA.

[44]  Maria Schuld,et al.  Implementing a distance-based classifier with a quantum interference circuit , 2017, 1703.10793.

[45]  Daniel A. Lidar,et al.  Solving a Higgs optimization problem with quantum annealing for machine learning , 2017, Nature.

[46]  Iordanis Kerenidis,et al.  Quantum Recommendation Systems , 2016, ITCS.

[47]  Krysta Marie Svore,et al.  Quantum Speed-Ups for Solving Semidefinite Programs , 2017, 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS).

[48]  Andreas Salzburger,et al.  ACTS: from ATLAS software towards a common track reconstruction software , 2017 .

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

[50]  D. Whiteson,et al.  Deep Learning and Its Application to LHC Physics , 2018, Annual Review of Nuclear and Particle Science.

[51]  Nathan Killoran,et al.  PennyLane: Automatic differentiation of hybrid quantum-classical computations , 2018, ArXiv.

[52]  R. Pooser,et al.  Cloud Quantum Computing of an Atomic Nucleus. , 2018, Physical Review Letters.

[53]  P. Rebentrost,et al.  Quantum machine learning for quantum anomaly detection , 2017, 1710.07405.

[54]  Keisuke Fujii,et al.  Quantum circuit learning , 2018, Physical Review A.

[55]  Hartmut Neven,et al.  Classification with Quantum Neural Networks on Near Term Processors , 2018, 1802.06002.

[56]  Rupak Biswas,et al.  Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers , 2017, Quantum Science and Technology.

[57]  Prabhat,et al.  Novel deep learning methods for track reconstruction , 2018, 1810.06111.

[58]  Ewin Tang,et al.  A quantum-inspired classical algorithm for recommendation systems , 2018, Electron. Colloquium Comput. Complex..

[59]  Simone Severini,et al.  Hierarchical quantum classifiers , 2018, npj Quantum Information.

[60]  Patrick J. Coles,et al.  Learning the quantum algorithm for state overlap , 2018, New Journal of Physics.

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

[62]  Lei Wang,et al.  Differentiable Learning of Quantum Circuit Born Machine , 2018, Physical Review A.

[63]  Kazuhiro Terao,et al.  Machine learning at the energy and intensity frontiers of particle physics , 2018, Nature.

[64]  L-M Duan,et al.  A quantum machine learning algorithm based on generative models , 2018, Science Advances.

[65]  Eli Upfal,et al.  Machine Learning in High Energy Physics Community White Paper , 2018, Journal of Physics: Conference Series.

[66]  J. Cirac,et al.  Neural-Network Quantum States, String-Bond States, and Chiral Topological States , 2017, 1710.04045.

[67]  Simone Severini,et al.  Quantum machine learning: a classical perspective , 2017, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

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

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

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

[71]  Cécile Germain,et al.  The Tracking Machine Learning Challenge: Accuracy Phase , 2019, The NeurIPS '18 Competition.

[72]  Paolo Calafiura,et al.  Quantum Associative Memory in Hep Track Pattern Recognition , 2019, EPJ Web of Conferences.

[73]  R. Sipio,et al.  Unfolding measurement distributions via quantum annealing , 2019, Journal of High Energy Physics.

[74]  Joshua Job,et al.  Restricted Boltzmann Machines for galaxy morphology classification with a quantum annealer , 2019, ArXiv.

[75]  A. Roggero,et al.  Dynamic linear response quantum algorithm , 2018, Physical Review C.

[76]  John Preskill,et al.  Simulating quantum field theory with a quantum computer , 2018, Proceedings of The 36th Annual International Symposium on Lattice Field Theory — PoS(LATTICE2018).

[77]  Miron Livny,et al.  Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware , 2019, Proceedings of 40th International Conference on High Energy physics — PoS(ICHEP2020).

[78]  K. Bibber,et al.  Putting the squeeze on axions , 2019, Physics Today.

[79]  Sioni Summers,et al.  Kalman Filter track reconstruction on FPGAs for acceleration of the High Level Trigger of the CMS experiment at the HL-LHC , 2019, EPJ Web of Conferences.

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

[81]  Alex Monras,et al.  Unsupervised Classification of Quantum Data , 2019, Physical Review X.

[82]  Daniel A. Lidar,et al.  Quantum adiabatic machine learning with zooming , 2019, ArXiv.

[83]  Arthur Pesah.  Learning quantum state properties with quantum and classical neural networks , 2019 .

[84]  Chad Rigetti,et al.  Automated quantum programming via reinforcement learning for combinatorial optimization , 2019, ArXiv.

[85]  Heather Gray,et al.  A Pattern Recognition Algorithm for Quantum Annealers , 2019, Computing and Software for Big Science.

[86]  Andrew M. Weiner,et al.  Simulations of subatomic many-body physics on a quantum frequency processor , 2018, Physical Review A.

[87]  C. Gogolin,et al.  Evaluating analytic gradients on quantum hardware , 2018, Physical Review A.

[88]  Soonwon Choi,et al.  Quantum convolutional neural networks , 2018, Nature Physics.

[89]  Alán Aspuru-Guzik,et al.  Quantum Chemistry in the Age of Quantum Computing. , 2018, Chemical reviews.

[90]  Tobias J. Osborne,et al.  No Free Lunch for Quantum Machine Learning , 2020, 2003.14103.

[91]  Seth Lloyd,et al.  Quantum-inspired algorithms in practice , 2019, Quantum.

[92]  Andrea Rocchetto,et al.  Fast quantum learning with statistical guarantees , 2020, ArXiv.

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

[94]  Shouvanik Chakrabarti,et al.  Quantum algorithms and lower bounds for convex optimization , 2018, Quantum.

[95]  Quantum embeddings for machine learning , 2020, 2001.03622.

[96]  B. Demirkoz,et al.  Particle Track Reconstruction with Quantum Algorithms , 2020, EPJ Web of Conferences.

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

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

[99]  C. Scheuerlein,et al.  Metallographic analysis of 11 T dipole coils for High Luminosity-Large Hadron Collider (HL-LHC) , 2020 .

[100]  David Von Dollen,et al.  TensorFlow Quantum: A Software Framework for Quantum Machine Learning , 2020, ArXiv.

[101]  Quantum computing for neutrino-nucleus scattering , 2019, Physical Review D.

[102]  Andrew W. Cross,et al.  Challenges and Opportunities of Near-Term Quantum Computing Systems , 2019, Proceedings of the IEEE.

[103]  Alán Aspuru-Guzik,et al.  Quantum computational chemistry , 2018, Reviews of Modern Physics.

[104]  Patrick J. Coles,et al.  Variational Quantum Fidelity Estimation , 2019, Quantum.

[105]  Jesse R. Stryker,et al.  SU(2) non-Abelian gauge field theory in one dimension on digital quantum computers , 2019, Physical Review D.

[106]  David Gosset,et al.  Classical algorithms for quantum mean values , 2019, Nature Physics.

[107]  John Napp,et al.  Low-Depth Gradient Measurements Can Improve Convergence in Variational Hybrid Quantum-Classical Algorithms. , 2019, Physical review letters.

[108]  B. Nachman,et al.  Quantum Algorithm for High Energy Physics Simulations. , 2019, Physical review letters.

[109]  Daniel A. Lidar,et al.  Charged particle tracking with quantum annealing optimization , 2019, Quantum Machine Intelligence.