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Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group (2307.03236v1)

Published 6 Jul 2023 in quant-ph, hep-ex, hep-lat, and hep-th

Abstract: Quantum computers offer an intriguing path for a paradigmatic change of computing in the natural sciences and beyond, with the potential for achieving a so-called quantum advantage, namely a significant (in some cases exponential) speed-up of numerical simulations. The rapid development of hardware devices with various realizations of qubits enables the execution of small scale but representative applications on quantum computers. In particular, the high-energy physics community plays a pivotal role in accessing the power of quantum computing, since the field is a driving source for challenging computational problems. This concerns, on the theoretical side, the exploration of models which are very hard or even impossible to address with classical techniques and, on the experimental side, the enormous data challenge of newly emerging experiments, such as the upgrade of the Large Hadron Collider. In this roadmap paper, led by CERN, DESY and IBM, we provide the status of high-energy physics quantum computations and give examples for theoretical and experimental target benchmark applications, which can be addressed in the near future. Having the IBM 100 x 100 challenge in mind, where possible, we also provide resource estimates for the examples given using error mitigated quantum computing.

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References (187)
  1. I. Quantum, Quantum-centric supercomputing: The next wave of computing (a).
  2. C. Alexandrou, Recent progress on the study of nucleon structure from lattice QCD and future perspectives, SciPost Phys. Proc. , 015 (2020).
  3. K. Fukushima and T. Hatsuda, The phase diagram of dense qcd, Rep. Prog. Phys. 74, 014001 (2011).
  4. M. Troyer and U.-J. Wiese, Computational complexity and fundamental limitations to fermionic quantum monte carlo simulations, Phys. Rev. Lett. 94, 170201 (2005).
  5. J. Kogut and L. Susskind, Hamiltonian formulation of wilson’s lattice gauge theories, Phys. Rev. D 11, 395 (1975).
  6. J. B. Kogut, An introduction to lattice gauge theory and spin systems, Rev. Mod. Phys. 51, 659 (1979).
  7. M. Dalmonte and S. Montangero, Lattice gauge theory simulations in the quantum information era, Contemp. Phys. 57, 388 (2016).
  8. M. C. Bañuls and K. Cichy, Review on novel methods for lattice gauge theories, Rep. Prog. Phys. 83, 024401 (2020).
  9. T. Byrnes and Y. Yamamoto, Simulating lattice gauge theories on a quantum computer, Phys. Rev. A 73, 022328 (2006).
  10. S. P. Jordan, K. S. M. Lee, and J. Preskill, Quantum algorithms for quantum field theories, Science 336, 1130 (2012).
  11. S. V. Mathis, G. Mazzola, and I. Tavernelli, Toward scalable simulations of lattice gauge theories on quantum computers, Phys. Rev. D 102, 094501 (2020).
  12. R. P. Feynman, Simulating physics with computers, Int. J. Theor. Phys. 21, 467 (1982).
  13. N. Klco, J. R. Stryker, and M. J. Savage, Su(2) non-abelian gauge field theory in one dimension on digital quantum computers, Phys. Rev. D 101, 074512 (2020).
  14. A. Ciavarella, N. Klco, and M. J. Savage, Trailhead for quantum simulation of su(3) yang-mills lattice gauge theory in the local multiplet basis, Phys. Rev. D 103, 094501 (2021).
  15. G. Clemente, A. Crippa, and K. Jansen, Strategies for the determination of the running coupling of (2+1)-dimensional QED with quantum computing, Phys. Rev. D 106, 114511 (2022).
  16. E. Zohar and B. Reznik, Confinement and lattice quantum-electrodynamic electric flux tubes simulated with ultracold atoms, Phys. Rev. Lett. 107, 275301 (2011).
  17. E. Zohar, J. I. Cirac, and B. Reznik, Simulating compact quantum electrodynamics with ultracold atoms: Probing confinement and nonperturbative effects, Phys. Rev. Lett. 109, 125302 (2012).
  18. E. Zohar, J. I. Cirac, and B. Reznik, Cold-atom quantum simulator for su(2) yang-mills lattice gauge theory, Phys. Rev. Lett. 110, 125304 (2013a).
  19. E. Zohar, J. I. Cirac, and B. Reznik, Simulating (2+1212+12 + 1)-dimensional lattice qed with dynamical matter using ultracold atoms, Phys. Rev. Lett. 110, 055302 (2013b).
  20. E. Zohar, J. I. Cirac, and B. Reznik, Quantum simulations of lattice gauge theories using ultracold atoms in optical lattices, Rep. Prog. Phys. 79, 014401 (2016).
  21. D. González-Cuadra, E. Zohar, and J. I. Cirac, Quantum simulation of the abelian-higgs lattice gauge theory with ultracold atoms, New J. Phys. 19, 063038 (2017).
  22. E. Rico, M. Dalmonte, P. Zoller, D. Banerjee, M. Bögli, P. Stebler, and U.-J. Wiese, So(3) “nuclear physics” with ultracold gases, Ann. Phys. 393, 466 (2018).
  23. J. Gambetta, IBM’s roadmap for scaling quantum technology.
  24. I. Newsroom, IBM unveils 400 qubit-plus quantum processor and next-generation IBM Quantum System Two.
  25. I. Quantum, The IBM Quantum Development Roadmap (b).
  26. W. G. Unruh, Maintaining coherence in quantum computers, Phys. Rev. A 51, 992 (1995).
  27. P. W. Shor, Algorithms for quantum computation: discrete logarithms and factoring, in Proceedings 35th annual symposium on foundations of computer science (Ieee, 1994) pp. 124–134.
  28. G. Brassard and P. Hoyer, An exact quantum polynomial-time algorithm for simon’s problem, in Proceedings of the Fifth Israeli Symposium on Theory of Computing and Systems (IEEE, 1997) pp. 12–23.
  29. L. K. Grover, Quantum computers can search rapidly by using almost any transformation, Phys. Rev. Lett. 80, 4329 (1998).
  30. A. Y. Kitaev, Quantum measurements and the abelian stabilizer problem, arXiv:quant-ph/9511026 ,  (1995).
  31. C. Gidney and M. Ekerå, How to factor 2048 bit rsa integers in 8 hours using 20 million noisy qubits, Quantum 5, 433 (2021).
  32. N. P. Breuckmann and J. N. Eberhardt, Quantum low-density parity-check codes, PRX Quantum 2, 040101 (2021).
  33. Y. Li and S. C. Benjamin, Efficient variational quantum simulator incorporating active error minimization, Phys. Rev. X 7, 021050 (2017).
  34. K. Temme, S. Bravyi, and J. M. Gambetta, Error mitigation for short-depth quantum circuits, Phys. Rev. Lett. 119, 180509 (2017).
  35. L. Viola and S. Lloyd, Dynamical suppression of decoherence in two-state quantum systems, Phys. Rev. A 58, 2733 (1998).
  36. L. Viola, E. Knill, and S. Lloyd, Dynamical decoupling of open quantum systems, Phys. Rev. Lett. 82, 2417 (1999).
  37. E. Knill, Fault-tolerant postselected quantum computation: Threshold analysis (2004).
  38. J. Preskill, Quantum Computing in the NISQ era and beyond, Quantum 2, 79 (2018).
  39. S. Lloyd, Universal quantum simulators, Science 273, 1073 (1996).
  40. U. Schollwöck, The density-matrix renormalization group in the age of matrix product states, Ann. Phys. 326, 96 (2011).
  41. S. Montangero, Introduction to Tensor Network Methods (Springer International Publishing, Cham, 2018).
  42. Y. Aoki et al. (Flavour Lattice Averaging Group (FLAG)), FLAG Review 2021, Eur. Phys. J. C 82, 869 (2022).
  43. S. Schaefer, R. Sommer, and F. Virotta (ALPHA), Critical slowing down and error analysis in lattice QCD simulations, Nucl. Phys. B 845, 93 (2011).
  44. I. M. Georgescu, S. Ashhab, and F. Nori, Quantum simulation, Reviews of Modern Physics 86, 153 (2014).
  45. C. Gross and W. S. Bakr, Quantum gas microscopy for single atom and spin detection, Nature Physics 17, 1316 (2021).
  46. I. Bloch, Quantum coherence and entanglement with ultracold atoms in optical lattices, Nature 453, 1016 (2008).
  47. A. Browaeys and T. Lahaye, Many-body physics with individually controlled rydberg atoms, Nature Physics 16, 132 (2020).
  48. S. D. Bass and E. Zohar, Quantum technologies in particle physics (2022).
  49. J. C. Halimeh and P. Hauke, Reliability of lattice gauge theories, Phys. Rev. Lett. 125, 030503 (2020).
  50. H. Nielsen and M. Ninomiya, A no-go theorem for regularizing chiral fermions, Physics Letters B 105, 219 (1981).
  51. L. Susskind, Lattice fermions, Phys. Rev. D 16, 3031 (1977).
  52. H. J. Rothe, Lattice gauge theories: an introduction (World Scientific Publishing Company, 2012).
  53. K. G. Wilson, Confinement of quarks, Phys. Rev. D 10, 2445 (1974).
  54. C. W. Bauer and D. M. Grabowska, Efficient representation for simulating U(1) gauge theories on digital quantum computers at all values of the coupling, Phys. Rev. D 107, L031503 (2023).
  55. O. Higgott, D. Wang, and S. Brierley, Variational quantum computation of excited states, Quantum 3, 156 (2019).
  56. K. M. Nakanishi, K. Mitarai, and K. Fujii, Subspace-search variational quantum eigensolver for excited states, Phys. Rev. Res. 1, 033062 (2019).
  57. A. Kan and Y. Nam, Lattice quantum chromodynamics and electrodynamics on a universal quantum computer, arXiv:2107.12769 ,  (2021).
  58. Z. Davoudi, I. Raychowdhury, and A. Shaw, Search for efficient formulations for hamiltonian simulation of non-abelian lattice gauge theories, Phys. Rev. D 104, 074505 (2021).
  59. Z. Davoudi, A. F. Shaw, and J. R. Stryker, General quantum algorithms for hamiltonian simulation with applications to a non-abelian lattice gauge theory, arXiv:2212.14030 ,  (2022).
  60. T. V. Zache, D. González-Cuadra, and P. Zoller, Quantum and classical spin network algorithms for q𝑞qitalic_q-deformed kogut-susskind gauge theories, arXiv:2304.02527 ,  (2023a).
  61. T. V. Zache, D. González-Cuadra, and P. Zoller, Fermion-qudit quantum processors for simulating lattice gauge theories with matter, arXiv:2303.08683 ,  (2023b).
  62. D. Horn, Finite matrix models with continuous local gauge invariance, Physics Letters B 100, 149 (1981).
  63. P. Orland and D. Rohrlich, Lattice gauge magnets: Local isospin from spin, Nucl. Phys. B 338, 647 (1990).
  64. S. Chandrasekharan and U.-J. Wiese, Quantum link models: A discrete approach to gauge theories, Nucl. Phys. B 492, 455 (1997).
  65. R. Brower, S. Chandrasekharan, and U.-J. Wiese, Qcd as a quantum link model, Phys. Rev. D 60, 094502 (1999).
  66. U.-J. Wiese, Ultracold quantum gases and lattice systems: quantum simulation of lattice gauge theories, Annalen der Physik 525, 777 (2013).
  67. U.-J. Wiese, Towards quantum simulating qcd, Nuclear Physics A 931, 246 (2014).
  68. U.-J. Wiese, From quantum link models to d-theory: a resource efficient framework for the quantum simulation and computation of gauge theories, Philosophical Transactions of the Royal Society A 380, 20210068 (2022).
  69. A. d’Adda, M. Lüscher, and P. Di Vecchia, A 1n expandable series of non-linear σ𝜎\sigmaitalic_σ models with instantons, Nucl. Phys. B 146, 63 (1978).
  70. H. Eichenherr, Su (n) invariant non-linear σ𝜎\sigmaitalic_σ models, Nucl. Phys. B 146, 215 (1978).
  71. H. Duan, G. M. Fuller, and Y.-Z. Qian, Collective neutrino oscillations, Annual Review of Nuclear and Particle Science 60, 569 (2010).
  72. I. Tamborra and S. Shalgar, New developments in flavor evolution of a dense neutrino gas, Annual Review of Nuclear and Particle Science 71, 165 (2021).
  73. P. Siwach, A. M. Suliga, and A. B. Balantekin, Entanglement in three-flavor collective neutrino oscillations, arXiv preprint arXiv:2211.07678  (2022).
  74. T. Stirner, G. Sigl, and G. Raffelt, Liouville term for neutrinos: flavor structure and wave interpretation, Journal of Cosmology and Astroparticle Physics 2018 (05), 016.
  75. A. V. Patwardhan, M. J. Cervia, and A. B. Balantekin, Spectral splits and entanglement entropy in collective neutrino oscillations, Phys. Rev. D 104, 123035 (2021).
  76. A. Rajput, A. Roggero, and N. Wiebe, Hybridized Methods for Quantum Simulation in the Interaction Picture, Quantum 6, 780 (2022).
  77. A. Roggero, Entanglement and many-body effects in collective neutrino oscillations, Phys. Rev. D 104, 103016 (2021).
  78. M. Illa and M. J. Savage, Multi-neutrino entanglement and correlations in dense neutrino systems, Phys. Rev. Lett. 130, 221003 (2023).
  79. M. Illa and M. J. Savage, Basic elements for simulations of standard-model physics with quantum annealers: Multigrid and clock states, Phys. Rev. A 106, 052605 (2022).
  80. M. Farina, Y. Nakai, and D. Shih, Searching for new physics with deep autoencoders, Phys. Rev. D 101, 075021 (2020).
  81. M. Britsch, N. Gagunashvili, and M. Schmelling, Application of the rule-growing algorithm RIPPER to particle physics analysis, PoS ACAT08, 086 (2008), arXiv:0910.1729 [physics.data-an] .
  82. M. Britsch, N. Gagunashvili, and M. Schmelling, Classifying extremely imbalanced data sets, arXiv:1011.6224 ,  (2010).
  83. J. P. Edelen and N. M. Cook, Anomaly Detection in Particle Accelerators using Autoencoders, arXiv:2112.07793 ,  (2021).
  84. J. H. Collins, K. Howe, and B. Nachman, Extending the search for new resonances with machine learning, Phys. Rev. D 99, 014038 (2019).
  85. H. M. Nguyen, E. W. Cooper, and K. Kamei, Borderline over-sampling for imbalanced data classification, Int. J. Knowl. Eng. Soft Data Paradigms 3, 4 (2009).
  86. J. Vazquez-Escobar, J. Hernandez, and M. Cardenas-Montes, Estimation of machine learning model uncertainty in particle physics event classifiers, Computer Physics Communications 268, 108100 (2021).
  87. A. Ballestrero and M. G. et al., Precise predictions for same-sign W-boson scattering at the LHC, The European Physical Journal C 78, 671 (2018).
  88. M. Aaboud et al. (ATLAS Collaboration), Search for the decay of the higgs boson to charm quarks with the atlas experiment, Phys. Rev. Lett. 120, 211802 (2018).
  89. E. M. Metodiev, B. Nachman, and J. Thaler, Classification without labels: learning from mixed samples in high energy physics, J. High Energy Phys. 2017 (10), 174.
  90. V. S. Ngairangbam, M. Spannowsky, and M. Takeuchi, Anomaly detection in high-energy physics using a quantum autoencoder, Phys. Rev. D 105, 095004 (2022).
  91. M. Schuld and N. Killoran, Quantum machine learning in feature hilbert spaces, Phys. Rev. Lett. 122, 040504 (2019).
  92. A. Crippa et al., Quantum algorithms for charged particle track reconstruction in the LUXE experiment, arXiv:2304.01690 ,  (2023).
  93. D. Magano et al., Quantum speedup for track reconstruction in particle accelerators, Phys. Rev. D 105, 076012 (2022), arXiv:2104.11583 [quant-ph] .
  94. T. C. Collaboration, Description and performance of track and primary-vertex reconstruction with the cms tracker, Journal of Instrumentation 9 (10), 10009.
  95. New versions have been published [412], but the general analysis should hold also in this case.
  96. G. Sguazzoni, Track reconstruction in CMS high luminosity environment, Nuclear and Particle Physics Proceedings 273-275, 2497 (2016).
  97. T. A. Collaboration, Performance of the ATLAS track reconstruction algorithms in dense environments in LHC Run 2, European Physical Journal C 77, 10.1140/epjc/s10052-017-5225-7 (2017), arXiv:arXiv:1704.07983v2 .
  98. N. Braun, Combinatorial Kalman Filter and High Level Trigger Reconstruction for the Belle II Experiment, Springer Theses (Springer International Publishing, Cham, 2019).
  99. E. Farhi, Quantum chromodynamics test for jets, Phys. Rev. Lett. 39, 1587 (1977).
  100. H. Yamamoto, An efficient algorithm for calculating thrust in high multiplicity reactions, Journal of Computational Physics 52, 597 (1983).
  101. G. P. Salam and G. Soyez, A practical seedless infrared-safe cone jet algorithm, J. High Energy Phys. 2007 (05), 086–086.
  102. A. Delgado and J. Thaler, Quantum annealing for jet clustering with thrust, Phys. Rev. D 106, 094016 (2022).
  103. D. Pires, Y. Omar, and J. Seixas, Adiabatic quantum algorithm for multijet clustering in high energy physics, arXiv:2012.14514  (2020).
  104. J. J. M. de Lejarza, L. Cieri, and G. Rodrigo, Quantum clustering and jet reconstruction at the LHC, Phys. Rev. D 106, 036021 (2022), arXiv:2204.06496 [hep-ph] .
  105. J. B. MacQueen, Some methods for classification and analysis of multivariate observations, in Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, edited by L. M. L. Cam and J. Neyman (University of California Press, 1967) pp. 281–297.
  106. B. J. Frey and D. Dueck, Clustering by passing messages between data points, Science 315, 972 (2007), https://www.science.org/doi/pdf/10.1126/science.1136800 .
  107. S. D. Ellis and D. E. Soper, Successive combination jet algorithm for hadron collisions, Phys. Rev. D 48, 3160 (1993).
  108. M. Cacciari, G. P. Salam, and G. Soyez, FastJet user manual, The European Physical Journal C 72, 10.1140/epjc/s10052-012-1896-2 (2012).
  109. D. J. Gross and F. Wilczek, Ultraviolet Behavior of Nonabelian Gauge Theories, Phys. Rev. Lett. 30, 1343 (1973a).
  110. D. J. Gross and F. Wilczek, Asymptotically Free Gauge Theories - I, Phys. Rev. D 8, 3633 (1973b).
  111. M. A. Shifman, A. I. Vainshtein, and V. I. Zakharov, QCD and Resonance Physics. Theoretical Foundations, Nucl. Phys. B 147, 385 (1979).
  112. J. Collins, Foundations of Perturbative QCD, Cambridge Monographs on Particle Physics, Nuclear Physics and Cosmology (Cambridge University Press, 2011).
  113. A. Buckley, C. White, and M. White, Practical Collider Physics, 2053-2563 (IOP Publishing, 2021).
  114. J. C. Criado, R. Kogler, and M. Spannowsky, Quantum fitting framework applied to effective field theories, Phys. Rev. D 107, 015023 (2023), arXiv:2207.10088 [hep-ph] .
  115. I. Brivio and M. Trott, The Standard Model as an Effective Field Theory, Phys. Rept. 793, 1 (2019), arXiv:1706.08945 [hep-ph] .
  116. J. A. e. a. The HEP Software Foundation, A roadmap for HEP software and computing R & D for the 2020s., Comput. Softw. Big Sci. 3(1), 7 (2019).
  117. B. Schmidt, The high-luminosity upgrade of the lhc: Physics and technology challenges for the accelerator and the experiments, in Journal of Physics: Conference Series, Vol. 706 (IOP Publishing, 2016) p. 022002.
  118. C. Krause and D. Shih, Caloflow: fast and accurate generation of calorimeter showers with normalizing flows, arXiv preprint arXiv:2106.05285  (2021).
  119. A. e. a. Delgado, Quantum computing for data analysis in high energy physics (2022).
  120. G. H. Low and I. L. Chuang, Optimal hamiltonian simulation by quantum signal processing, Phys. Rev. Lett. 118, 010501 (2017).
  121. G. H. Low and I. L. Chuang, Hamiltonian Simulation by Qubitization, Quantum 3, 163 (2019).
  122. L. Nagano, A. Bapat, and C. W. Bauer, Quench dynamics of the schwinger model via variational quantum algorithms, arXiv preprint  (2023), arXiv:2302.10933 .
  123. H. F. Trotter, On the product of semi-groups of operators, Proceedings of the American Mathematical Society 10, 545 (1959).
  124. C. Zhang, Randomized algorithms for hamiltonian simulation, in Monte Carlo and Quasi-Monte Carlo Methods 2010 (Springer, 2012) pp. 709–719.
  125. A. M. Childs, A. Ostrander, and Y. Su, Faster quantum simulation by randomization, Quantum 3, 182 (2019).
  126. E. Campbell, Random compiler for fast hamiltonian simulation, Phys. Rev. Lett. 123, 070503 (2019).
  127. S. A. Chin, Multi-product splitting and runge-kutta-nyström integrators, Celestial Mechanics and Dynamical Astronomy 106, 391 (2010).
  128. S. Endo, I. Kurata, and Y. O. Nakagawa, Calculation of the green’s function on near-term quantum computers, Phys. Rev. Research 2, 033281 (2020).
  129. A. Miessen, P. J. Ollitrault, and I. Tavernelli, Quantum algorithms for quantum dynamics: A performance study on the spin-boson model, Phys. Rev. Research 3, 043212 (2021).
  130. N. Gomes, D. B. Williams-Young, and W. A. de Jong, Computing the many-body green’s function with adaptive variational quantum dynamics, arXiv:2302.03093 ,  (2023).
  131. C. Zoufal, D. Sutter, and S. Woerner, Error bounds for variational quantum time evolution, arXiv preprint  (2021), arXiv:2108.00022 .
  132. M. Otten, C. L. Cortes, and S. K. Gray, Noise-resilient quantum dynamics using symmetry-preserving ansatzes, arXiv preprint arXiv:1910.06284  (2019).
  133. S. Barison, F. Vicentini, and G. Carleo, An efficient quantum algorithm for the time evolution of parameterized circuits, arXiv preprint arXiv:2101.04579  (2021).
  134. M. Benedetti, M. Fiorentini, and M. Lubasch, Hardware-efficient variational quantum algorithms for time evolution, arXiv preprint arXiv:2009.12361  (2020).
  135. K. Bharti and T. Haug, Quantum-assisted simulator, Phys. Rev. A 104, 042418 (2021).
  136. T. Haug and K. Bharti, Generalized quantum assisted simulator, arXiv preprint arXiv:2011.14737  (2020).
  137. N. Quadri, Master thesis (2021), ETH Zurich.
  138. S. Aaronson, Read the fine print, Nature Phys. 11, 291 (2015).
  139. V. Giovannetti, S. Lloyd, and L. Maccone, Quantum random access memory, Phys. Rev. Lett. 100, 160501 (2008).
  140. Y. Liu, S. Arunachalam, and K. Temme, A rigorous and robust quantum speed-up in supervised machine learning, Nature Physics , 1 (2021).
  141. C. Gyurik and V. Dunjko, On establishing learning separations between classical and quantum machine learning with classical data, arXiv:2208.06339 ,  (2022).
  142. G. V. Cybenko, Approximation by superpositions of a sigmoidal function, Mathematics of Control, Signals and Systems 2, 303 (1989).
  143. S. C. Marshall, C. Gyurik, and V. Dunjko, High Dimensional Quantum Machine Learning With Small Quantum Computers, arXiv:2203.13739 ,  (2022).
  144. M. Cerezo and P. J. Coles, Higher order derivatives of quantum neural networks with barren plateaus, Quantum Science and Technology 6, 035006 (2021).
  145. C. Ortiz Marrero, M. Kieferová, and N. Wiebe, Entanglement-induced barren plateaus, PRX Quantum 2, 040316 (2021).
  146. D. Stilck França and R. Garcia-Patron, Limitations of optimization algorithms on noisy quantum devices, Nature Physics 17, 1221 (2021).
  147. E. Cervero Martín, K. Plekhanov, and M. Lubasch, Barren plateaus in quantum tensor network optimization, arXiv e-prints , arXiv:2209.00292 (2022), arXiv:2209.00292 [quant-ph] .
  148. Y.-F. Niu, S. Zhang, and W.-S. Bao, Warm starting variational quantum algorithms with near clifford circuits, Electronics 12, 347 (2023).
  149. L. Bittel and M. Kliesch, Training variational quantum algorithms is np-hard, Phys. Rev. Lett. 127, 120502 (2021).
  150. X. You and X. Wu, Exponentially many local minima in quantum neural networks, in International Conference on Machine Learning (PMLR, 2021) pp. 12144–12155.
  151. E. R. Anschuetz and B. T. Kiani, Beyond barren plateaus: Quantum variational algorithms are swamped with traps, arXiv preprint arXiv:2205.05786  (2022).
  152. P. Mernyei, K. Meichanetzidis, and İsmail İlkan Ceylan, Equivariant quantum graph circuits (2022), arXiv:2112.05261 [cs.LG] .
  153. R. O’Donnell and J. Wright, Efficient quantum tomography, in Proceedings of the forty-eighth annual ACM symposium on Theory of Computing (2016) pp. 899–912.
  154. A. Montanaro, Learning stabilizer states by Bell sampling, arXiv:1707.04012 ,  (2017).
  155. S. Aaronson, Shadow tomography of quantum states, in Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing, STOC (ACM, 2018) pp. 325–338.
  156. S. Aaronson, The learnability of quantum states, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 463, 3089–3114 (2007).
  157. A. Anshu and S. Arunachalam, A survey on the complexity of learning quantum states, arXiv:2305.20069 ,  (2023).
  158. I. Cong, S. Choi, and M. D. Lukin, Quantum convolutional neural networks, Nature Physics 15, 1273 (2019), arXiv:1810.03787 [quant-ph] .
  159. L. Banchi, J. Pereira, and S. Pirandola, Generalization in Quantum Machine Learning: A Quantum Information Standpoint, PRX Quantum 2, 040321 (2021), arXiv:2102.08991 [quant-ph] .
  160. A. V. Uvarov, A. S. Kardashin, and J. D. Biamonte, Machine learning phase transitions with a quantum processor, Phys. Rev. A 102, 012415 (2020).
  161. M. R. Geller and Z. Zhou, Efficient error models for fault-tolerant architectures and the pauli twirling approximation, Phys. Rev. A 88, 012314 (2013).
  162. H.-Y. Huang, R. Kueng, and J. Preskill, Predicting many properties of a quantum system from very few measurements, Nature Physics 16, 1050 (2020).
  163. E. Farhi, J. Goldstone, and S. Gutmann, A quantum approximate optimization algorithm, arXiv:1411.4028  (2014).
  164. E. Farhi and A. W. Harrow, Quantum supremacy through the quantum approximate optimization algorithm, arXiv:1602.07674  (2016).
  165. D. J. Egger, J. Mareček, and S. Woerner, Warm-starting quantum optimization, Quantum 5, 479 (2021).
  166. J. Wurtz and P. J. Love, Counterdiabaticity and the quantum approximate optimization algorithm, Quantum 6, 635 (2022).
  167. S. Lloyd, Least squares quantization in pcm, IEEE Transactions on Information Theory 28, 129 (1982).
  168. S. Lloyd, M. Mohseni, and P. Rebentrost, Quantum algorithms for supervised and unsupervised machine learning (2013).
  169. D. Kopczyk, Quantum machine learning for data scientists (2018).
  170. C. Durr and P. Hoyer, A quantum algorithm for finding the minimum (1996).
  171. J. Kübler, S. Buchholz, and B. Schölkopf, The inductive bias of quantum kernels, Advances in Neural Information Processing Systems 34, 12661 (2021).
  172. M. Schuld, Supervised quantum machine learning models are kernel methods, arXiv preprint arXiv:2101.11020  (2021).
  173. S. Lloyd and C. Weedbrook, Quantum generative adversarial learning, Phys. Rev. Lett. 121, 040502 (2018).
  174. S. Kullback and R. A. Leibler, On information and sufficiency, The annals of mathematical statistics 22, 10.1214/aoms/1177729694 (1951).
  175. J. Lin, Divergence measures based on the shannon entropy, IEEE Transactions on Information Theory 37, 145 (1991).
  176. A. Rényi, On measures of entropy and information, in Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Contributions to the Theory of Statistics (1961) pp. 547–562.
  177. M. Kieferova, O. M. Carlos, and N. Wiebe, Quantum generative training using rényi divergences, arXiv preprint arXiv:2106.09567  (2021).
  178. A. Skolik, S. Jerbi, and V. Dunjko, Quantum agents in the gym: a variational quantum algorithm for deep q-learning, Quantum 6, 720 (2022a).
  179. S. Y.-C. Chen, Quantum deep recurrent reinforcement learning, arXiv preprint arXiv:2210.14876  (2022).
  180. N. Sale, B. Lucini, and J. Giansiracusa, Probing center vortices and deconfinement in su(2) lattice gauge theory with persistent homology, arXiv preprint arXiv:2207.13392  (2022).
  181. S. Lloyd, S. Garnerone, and P. Zanardi, Quantum algorithms for topological and geometric analysis of data, Nat. Commun. 7, 10138 (2016).
  182. C. Gyurik, C. Cade, and V. Dunjko, Towards quantum advantage via topological data analysis, Quantum 6, 855 (2022).
  183. C. Cade and P. M. Crichigno, Complexity of supersymmetric systems and the cohomology problem, arXiv preprint arXiv:2107.00011  (2021).
  184. R. Hayakawa, Quantum algorithm for persistent betti numbers and topological data analysis, Quantum 6, 873 (2022).
  185. S. McArdle, A. Gilyén, and M. Berta, A streamlined quantum algorithm for topological data analysis with exponentially fewer qubits, arXiv preprint arXiv:2209.12887  (2022).
  186. M. Crichigno and T. Kohler, Clique homology is qma1-hard, arXiv preprint arXiv:2209.11793  (2022).
  187. E. Witten, Supersymmetry and morse theory, Journal of differential geometry 17, 661 (1982).
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Summary

  • The paper demonstrates novel quantum algorithms, including VQE and QAOA, to simulate complex lattice gauge theories.
  • It outlines quantum-enhanced methods for detector simulation and signal analysis in experimental high-energy physics.
  • The research emphasizes error mitigation and resource optimization as essential for achieving practical quantum advantage.

Overview of "Quantum Computing for High-Energy Physics: State of the Art and Challenges - Summary of the QC4HEP Working Group"

Introduction

The paper "Quantum Computing for High-Energy Physics: State of the Art and Challenges - Summary of the QC4HEP Working Group" provides an extensive exploration of the potential of quantum computing applications within the domain of high-energy physics (HEP). This research is propelled by the growing development of quantum computing hardware and its capacity to manage complex simulations unachievable by classical computing methods.

Quantum Computing in High-Energy Physics

The convergence of high-energy physics and quantum computing is being actively researched to address computationally demanding problems. Quantum simulations could fundamentally advance our understanding of quantum chromodynamics (QCD) and other quantum field theories, which are cornerstone frameworks in particle physics.

  1. Theoretical Modelling and Algorithms:
    • Tensor Networks (TN): Utilized for representing complex quantum states, TN presents an efficient framework for classical simulations of lattice gauge theories.
    • Quantum Link Models: An innovative approach employing quantum links serving as quantum spins to embed gauge symmetries, thus enabling paper through quantum complex networks.
    • Variational Quantum Eigensolver (VQE) and Variational Quantum Deflation (VQD): These algorithms are utilized for approximating low-energy solutions of quantum systems beyond the reach of classical approximations.
    • Quantum Approximate Optimization Algorithm (QAOA): A method being tailored for combinatorial optimization, QAOA's applicability in lattice gauge theories could represent significant utility in high-energy physics.
  2. Quantum Computing in Experimental HEP:
    • Pattern Recognition and Signal Analysis: Quantum techniques such as quantum-enhanced support vector machines (QSVMs) and quantum neural networks (QNNs) are assessed for their potential in processing large and complex HEP data.
    • Simulation and Detector Design: Quantum computing can simulate detector responses and assist in design optimization, thus refining predictive models of particle interactions.
  3. Case Studies and Benchmarks:
    • The paper evaluates models such as (2+1)D Quantum Electrodynamics (QED) and non-Abelian Yang-Mills theories, advocating their examination through quantum simulations to explore beyond current capabilities and ensure long-term advances towards practical quantum computing applications in HEP.

Algorithmic and Computational Challenges

Several technical challenges hinder broader deployment of quantum computing in HEP:

  • Error Mitigation: Techniques such as zero-noise extrapolation and probabilistic error cancellation remain crucial as quantum error correction is still beyond current practicality.
  • Resource Optimization: Efforts to encode large Hilbert spaces efficiently on quantum circuits and reduce the overhead of circuit depth while maintaining result accuracy are addressed.

Implications and Future Directions

This research enunciates several key points:

  • A systematic roadmap is outlined for problem-specific applications in HEP, ranging from theoretical model simulation to experimental data processing. Benefits of quantum computing in reducing computation times and managing complex integrations are underscored.
  • Future developments should focus on algorithmic improvements, hardware advancements, and formulating more compact quantum representations of theories, all aimed at achieving practical quantum advantage.
  • The paper promotes collaborative efforts and underlines the necessity for interdisciplinary research combining quantum computing with HEP fields to unlock unexplored potentials.

Conclusion

The QC4HEP Working Group's summary asserts the transformative role quantum computing can play in advancing high-energy physics. By elucidating the current capabilities, challenges, and prospects, the research sets a foundation for continued exploration and development of quantum algorithms relevant to complex HEP processes, ultimately pushing the frontier of what can be achieved at the intersection of these two pivotal scientific domains.

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