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arxiv: 2605.06789 · v1 · submitted 2026-05-07 · 🪐 quant-ph · hep-ph· hep-th

Recognition: 2 theorem links

· Lean Theorem

Physics inspired quantum algorithm for QCD splitting functions

Authors on Pith no claims yet

Pith reviewed 2026-05-11 00:54 UTC · model grok-4.3

classification 🪐 quant-ph hep-phhep-th
keywords quantum circuitQCD splittingparton showerhelicity entanglementjet substructureevent generationquantum hardware
0
0 comments X

The pith

A two-qubit quantum circuit reproduces the helicity entanglement and momentum sharing of gluon splittings in QCD and composes into multi-prong distributions that match LHC jet data.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The authors build a simple quantum circuit to model the quantum correlations that arise when a gluon splits into two gluons. They first calculate the exact amount of entanglement created at the splitting vertex using a standard measure called concurrence. They then design a two-qubit circuit whose rotation angles directly control the momentum fractions carried by the two outgoing gluons while producing the same entanglement pattern predicted by QCD. When several copies of this circuit are placed in sequence, the combined measurement statistics generate the momentum distributions seen in three- and four-prong jets. The circuit is shallow enough to run on present-day superconducting processors, where it yields results consistent with both simulation and experimental data after routine error mitigation.

Core claim

For the pure-gluon channel, we derive an analytic expression for the helicity entanglement generated at the splitting vertex, quantified via the concurrence, and construct a two-qubit circuit whose measurement outcomes encode the momentum shared between outgoing gluons while reproducing the QCD-predicted entanglement structure. Calibrating the circuit parameters to LHC jet substructure data maps reconstructed momentum-sharing fractions to circuit rotation angles. Composing multiple splitting primitives yields multi-prong momentum-fraction distributions; we validate the three- and four-prong cases against experimental data and find good agreement. For the three-prong configuration, we execute

What carries the argument

A modular two-qubit circuit primitive whose parameterized gates encode momentum-sharing fractions and whose output statistics reproduce the concurrence of helicity entanglement at a gluon splitting vertex.

If this is right

  • Parameters calibrated once on single-splitting data produce multi-prong distributions that agree with measured jet substructure.
  • The shallow circuit depth allows direct execution on current superconducting hardware with results matching classical simulation after standard quality cuts.
  • The primitive supplies a physics-informed building block for constructing larger quantum circuits that simulate full parton showers.
  • Momentum fractions reconstructed from circuit measurements are directly tied to the rotation angles used in the gates.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same circuit structure could be extended to quark-gluon splitting channels by deriving the corresponding concurrence and adjusting the gate set.
  • If the composition rule holds at higher multiplicity, the approach offers a route to sampling rare jet configurations on quantum hardware without the exponential cost of classical Monte Carlo.
  • Jet observables sensitive to helicity correlations might serve as indirect probes of the entanglement generated at each splitting vertex.

Load-bearing premise

The entanglement and momentum mapping derived for one splitting can be directly chained across independent splittings without interference terms or higher-order QCD corrections altering the final distributions.

What would settle it

If the momentum-fraction histograms obtained by composing three or four copies of the calibrated circuit deviate measurably from LHC jet substructure data in the three- or four-prong channels, the composability assumption fails.

read the original abstract

We introduce a modular quantum circuit primitive to model entanglement dynamics in QCD parton splitting and use it as a composable building block for data-driven, physics-consistent event generation. For the pure-gluon channel, we derive an analytic expression for the helicity entanglement generated at the splitting vertex, quantified via the concurrence, and construct a two-qubit circuit whose measurement outcomes encode the momentum shared between outgoing gluons while reproducing the QCD-predicted entanglement structure. Calibrating the circuit parameters to LHC jet substructure data maps, reconstructed momentum-sharing fractions are directly related to circuit rotation angles. Composing multiple splitting primitives yields multi-prong momentum-fraction distributions; we validate the three- and four-prong cases against experimental data and find good agreement. For the three-prong configuration, we execute the circuit on superconducting quantum hardware and obtain results consistent with simulation after standard quality cuts, enabled by the low qubit count and shallow circuit depth. This work provides a concrete framework for quantum-native parton-shower modules that encode quantum correlations at the level of splitting dynamics, and offers physics-informed ans\"atze for future quantum algorithms for QCD.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 3 minor

Summary. The manuscript introduces a modular quantum circuit primitive for modeling helicity entanglement in QCD parton splitting. For the pure-gluon channel it derives an analytic concurrence, constructs a two-qubit circuit whose measurement statistics encode the momentum fraction z while matching that concurrence, calibrates the rotation angles to LHC jet substructure data, and composes multiple primitives to generate three- and four-prong momentum-fraction distributions that are reported to agree with experimental data; the three-prong case is also executed on superconducting hardware.

Significance. If the composition of calibrated single-splitting circuits reproduces multi-prong distributions beyond the fitted regime, the framework would supply a concrete, shallow-depth quantum-native module for encoding entanglement at splitting vertices and a physics-informed ansatz for future quantum parton-shower algorithms. The reported hardware execution with low qubit count and standard quality cuts demonstrates practical feasibility on current devices.

major comments (3)
  1. [§2] §2 (analytic concurrence): the claimed closed-form expression for the pure-gluon helicity concurrence is stated without derivation steps, intermediate spinor algebra, or explicit verification that it reduces to the known Altarelli–Parisi kernel in the appropriate limit; this derivation is load-bearing for the assertion that the circuit reproduces the QCD entanglement structure.
  2. [§4] §4 (calibration and multi-prong validation): rotation angles are fitted directly to LHC jet-substructure data to map z; the same primitives are then composed and compared to three- and four-prong distributions drawn from the same experimental class. No out-of-sample test, variation of calibration dataset, or comparison against an unfitted baseline is shown, so the reported agreement cannot yet be distinguished from a post-hoc fit.
  3. [§5] §5 (composition): the multi-prong results rest on the assumption that single-splitting entanglement and momentum sharing remain uncorrelated across successive vertices. No estimate of color-coherence or NLO interference terms is provided, nor is a comparison made to a full QCD shower with the same single-splitting kernel; this assumption is load-bearing for the claim that the composed circuit yields accurate multi-prong distributions.
minor comments (3)
  1. [Abstract] Abstract and §3: the phrase “physics-consistent” is used after data calibration; a brief clarification of what is meant by consistency (e.g., reproduction of the leading-order kernel plus fitted higher-order effects) would avoid ambiguity.
  2. [Hardware section] Hardware results paragraph: the quality cuts applied to the superconducting-device data are mentioned but not quantified; reporting the fraction of shots retained and the effect on the extracted z distribution would strengthen the comparison to simulation.
  3. [Throughout] Notation: the symbols for the calibrated rotation angles and the theoretical circuit angles are not always distinguished; a short table or explicit mapping would improve readability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the careful reading and constructive comments on our manuscript. We address each major comment point by point below, providing the strongest honest defense and indicating where revisions have been made to the manuscript.

read point-by-point responses
  1. Referee: §2 (analytic concurrence): the claimed closed-form expression for the pure-gluon helicity concurrence is stated without derivation steps, intermediate spinor algebra, or explicit verification that it reduces to the known Altarelli–Parisi kernel in the appropriate limit; this derivation is load-bearing for the assertion that the circuit reproduces the QCD entanglement structure.

    Authors: We agree that additional detail on the derivation is warranted. In the revised manuscript we have expanded §2 with the complete step-by-step spinor-algebra derivation of the concurrence and have added an explicit reduction to the Altarelli–Parisi kernel in the classical limit, thereby confirming that the circuit encodes the correct QCD entanglement structure. revision: yes

  2. Referee: §4 (calibration and multi-prong validation): rotation angles are fitted directly to LHC jet-substructure data to map z; the same primitives are then composed and compared to three- and four-prong distributions drawn from the same experimental class. No out-of-sample test, variation of calibration dataset, or comparison against an unfitted baseline is shown, so the reported agreement cannot yet be distinguished from a post-hoc fit.

    Authors: The calibration uses single-splitting observables while the multi-prong distributions constitute a test of composability on a different observable. To strengthen the presentation we have added, in the revision, a sensitivity analysis by varying the fitted parameters within their uncertainties and a direct comparison against an unfitted (flat-z) baseline. A fully independent out-of-sample dataset is not available within the current experimental samples, but we now explicitly discuss this limitation. revision: partial

  3. Referee: §5 (composition): the multi-prong results rest on the assumption that single-splitting entanglement and momentum sharing remain uncorrelated across successive vertices. No estimate of color-coherence or NLO interference terms is provided, nor is a comparison made to a full QCD shower with the same single-splitting kernel; this assumption is load-bearing for the claim that the composed circuit yields accurate multi-prong distributions.

    Authors: We acknowledge that the modular construction assumes uncorrelated successive splittings. In the revised manuscript we have inserted a paragraph providing an order-of-magnitude estimate of color-coherence and NLO interference effects drawn from the QCD literature, showing they remain small in the kinematic region probed. We have also added a qualitative comparison of the composed-circuit results to a standard parton-shower Monte Carlo that employs the identical single-splitting kernel. revision: yes

Circularity Check

1 steps flagged

Fitted single-splitting circuit parameters composed for multi-prong validation reduces to effective-model fit

specific steps
  1. fitted input called prediction [Abstract]
    "Calibrating the circuit parameters to LHC jet substructure data maps, reconstructed momentum-sharing fractions are directly related to circuit rotation angles. Composing multiple splitting primitives yields multi-prong momentum-fraction distributions; we validate the three- and four-prong cases against experimental data and find good agreement."

    Rotation angles are fitted to data to encode momentum fractions z at each vertex. The identical calibrated primitives are then composed without additional terms to generate multi-prong distributions that are validated against experimental data, so agreement is forced by the single-splitting fit rather than emerging from the analytic concurrence alone.

full rationale

The analytic derivation of concurrence and the two-qubit circuit construction that reproduces it appear independent of data. However, the load-bearing claim for multi-prong results is the direct composition of primitives whose rotation angles are calibrated to LHC jet substructure data; the reported agreement with three- and four-prong distributions therefore tests the extrapolation of a fitted single-splitting model rather than an unfitted first-principles composition. This matches the 'fitted input called prediction' pattern with partial circularity.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 1 invented entities

The central claim depends on fitting circuit parameters to experimental data and on the assumption that independent splitting primitives compose without extra quantum corrections.

free parameters (1)
  • circuit rotation angles
    Calibrated to LHC jet substructure data to directly relate to reconstructed momentum-sharing fractions.
axioms (2)
  • domain assumption QCD splitting functions determine a specific helicity entanglement structure that can be quantified by concurrence
    Invoked to derive the analytic expression for the pure-gluon channel.
  • domain assumption A two-qubit circuit can faithfully reproduce both the momentum distribution and the entanglement of the splitting vertex
    Basis for constructing the modular primitive.
invented entities (1)
  • modular quantum circuit primitive for parton splitting no independent evidence
    purpose: Composable building block that encodes quantum correlations for data-driven event generation
    New postulated module introduced to enable physics-consistent multi-prong simulations.

pith-pipeline@v0.9.0 · 5510 in / 1562 out tokens · 70964 ms · 2026-05-11T00:54:53.083906+00:00 · methodology

discussion (0)

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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

  • IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    For the pure-gluon channel, we derive an analytic expression for the helicity entanglement generated at the splitting vertex, quantified via the concurrence, and construct a two-qubit circuit whose measurement outcomes encode the momentum shared between outgoing gluons while reproducing the QCD-predicted entanglement structure.

  • IndisputableMonolith/Foundation/ArithmeticFromLogic.lean LogicNat.equivNat unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    Composing multiple splitting primitives yields multi-prong momentum-fraction distributions; we validate the three- and four-prong cases against experimental data and find good agreement.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

89 extracted references · 20 canonical work pages · 3 internal anchors

  1. [1]

    Preskill,Quantum Computing in the NISQ era and beyond,Quantum2(2018) 79

    J. Preskill,Quantum Computing in the NISQ era and beyond,Quantum2(2018) 79

  2. [2]

    Di Meglio, K

    A. Di Meglio, K. Jansen, I. Tavernelli, C. Alexandrou, S. Arunachalam, C.W. Bauer et al., Quantum Computing for High-Energy Physics: State of the Art and Challenges,PRX Quantum 5(2024) 037001

  3. [3]

    Nachman, D

    B. Nachman, D. Provasoli, W.A. de Jong and C.W. Bauer,Quantum Algorithm for High Energy Physics Simulations,Phys. Rev. Lett.126(2021) 062001

  4. [4]

    Bepari, S

    K. Bepari, S. Malik, M. Spannowsky and S. Williams,Towards a Quantum Computing Algorithm for Helicity Amplitudes and Parton Showers,Physical Review D103(2021) 076020

  5. [5]

    Bepari, S

    K. Bepari, S. Malik, M. Spannowsky and S. Williams,Quantum walk approach to simulating parton showers,Physical Review D106(2022) 056002

  6. [6]

    Bauer, S

    C.W. Bauer, S. Chigusa and M. Yamazaki,Quantum Parton Shower with Kinematics,Physical Review A109(2024) 032432

  7. [7]

    Chawdhry and M

    H.A. Chawdhry and M. Pellen,Quantum simulation of colour in perturbative quantum chromodynamics,SciPost Phys.15(2023) 205

  8. [8]

    Jordan, K.S.M

    S.P. Jordan, K.S.M. Lee and J. Preskill,Quantum Algorithms for Quantum Field Theories, Science336(2012) 1130–1133

  9. [9]

    García-Álvarez, J

    L. García-Álvarez, J. Casanova, A. Mezzacapo, I. Egusquiza, L. Lamata, G. Romero et al., Fermion-Fermion Scattering in Quantum Field Theory with Superconducting Circuits,Physical Review Letters114(2015)

  10. [10]

    Halimeh, M

    J.C. Halimeh, M. Hanada, S. Matsuura, F. Nori, E. Rinaldi and A. Schäfer,A Universal Framework for the Quantum Simulation of Yang-Mills Theory,Communications Physics9 (2026) 67

  11. [11]

    Tagliacozzo, A

    L. Tagliacozzo, A. Celi, P. Orland, M.W. Mitchell and M. Lewenstein,Simulation of Non-Abelian Gauge Theories with Optical Lattices,Nature Communications4(2013) 2615

  12. [12]

    Silvi, E

    P. Silvi, E. Rico, M. Dalmonte, F. Tschirsich and S. Montangero,Finite-Density Phase Diagram of a(1 + 1)D Non-Abelian Lattice Gauge Theory with Tensor Networks,Quantum1(2017) 9

  13. [13]

    Klco, J.R

    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. D101(2020) 074512

  14. [14]

    Schuhmacher, L

    J. Schuhmacher, L. Boggia, V. Belis, E. Puljak, M. Grossi, M. Pierini et al.,Unravelling physics beyond the standard model with classical and quantum anomaly detection,Machine Learning: Science and Technology4(2023) 045031. – 17 –

  15. [15]

    Tüysüz, C

    C. Tüysüz, C. Rieger, K. Novotny, B. Demirköz, D. Dobos, K. Potamianos et al.,Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction,Quantum Machine Intelligence3(2021) 29

  16. [16]

    Funcke, T

    L. Funcke, T. Hartung, B. Heinemann, K. Jansen, A. Kropf, S. Kühn et al.,Studying quantum algorithms for particle track reconstruction in the LUXE experiment,Journal of Physics: Conference Series2438(2023) 012127

  17. [17]

    Crippa, L

    A. Crippa, L. Funcke, T. Hartung, B. Heinemann, K. Jansen, A. Kropf et al.,Quantum Algorithms for Charged Particle Track Reconstruction in the LUXE Experiment,Computing and Software for Big Science7(2023) 14

  18. [18]

    A.Y. Wei, P. Naik, A.W. Harrow and J. Thaler,Quantum Algorithms for Jet Clustering, Physical Review D101(2020) 094015

  19. [19]

    Cheng, T

    K. Cheng, T. Han and M. Low,Quantum tomography at colliders: With or without decays, Phys. Lett. B868(2025) 139675 [2410.08303]

  20. [20]

    Magano, A

    D. Magano, A. Kumar, M. K¯ alis, A. Loc¯ ans, A. Glos, S. Pratapsi et al.,Quantum speedup for track reconstruction in particle accelerators,Physical Review D105(2022) 076012

  21. [21]

    Valassi, E

    A. Valassi, E. Yazgan, J. McFayden, S. Amoroso, J. Bendavid, A. Buckley et al.,Challenges in Monte Carlo Event Generator Software for High-Luminosity LHC,Computing and Software for Big Science5(2021) 12

  22. [22]

    Albrecht, A.A

    J. Albrecht, A.A. Alves, G. Amadio, G. Andronico, N. Anh-Ky, L. Aphecetche et al.,A Roadmap for HEP Software and Computing R&D for the 2020s,Computing and Software for Big Science3(2019) 7

  23. [23]

    P. Azzi, S. Farry, P. Nason, A. Tricoli, D. Zeppenfeld, R.A. Khalek et al.,Standard Model Physics at the HL-LHC and HE-LHC, Dec., 2019. 10.48550/arXiv.1902.04070

  24. [24]

    Buckley, C

    A. Buckley, C. White and M. White,Practical Collider Physics, IOP Publishing (2021), 10.1088/978-0-7503-2444-1

  25. [25]

    Andersson, G

    B. Andersson, G. Gustafson, G. Ingelman and T. Sjöstrand,Parton fragmentation and string dynamics,Physics Reports97(1983) 31

  26. [26]

    Field and S

    R.D. Field and S. Wolfram,A QCD model fore+e− annihilation,Nuclear Physics B213 (1983) 65

  27. [27]

    Mildenberger, W

    J. Mildenberger, W. Mruczkiewicz, J.C. Halimeh, Z. Jiang and P. Hauke,Confinement in aZ Lattice Gauge Theory on a Quantum Computer,Nature Physics21(2025) 312

  28. [28]

    Alexandrou, A

    C. Alexandrou, A. Athenodorou, K. Blekos, G. Polykratis and S. Kühn,Realizing String Breaking Dynamics in aZ2 Lattice Gauge Theory on Quantum Hardware,Physical Review D 112(2025) 114506

  29. [29]

    An Introduction to PYTHIA 8.2

    T. Sjöstrand, S. Ask, J.R. Christiansen, R. Corke, N. Desai, P. Ilten et al.,An Introduction to PYTHIA 8.2,Comput. Phys. Commun.191(2015) 159 [1410.3012]

  30. [30]

    M. Bähr, S. Gieseke, M.A. Gigg, D. Grellscheid, K. Hamilton, O. Latunde-Dada et al., Herwig++ physics and manual,The European Physical Journal C58(2008) 639–707

  31. [31]

    Gleisberg, S

    T. Gleisberg, S. Hoeche, F. Krauss, A. Schaelicke, S. Schumann and J. Winter,SHERPA 1., a proof-of-concept version,Journal of High Energy Physics2004(2004) 056–056. – 18 –

  32. [32]

    Ellis, W.J

    R.K. Ellis, W.J. Stirling and B.R. Webber,QCD and Collider Physics, Cambridge Monographs on Particle Physics, Nuclear Physics and Cosmology, Cambridge University Press (1996)

  33. [33]

    Low and T

    I. Low and T. Mehen,Symmetry from entanglement suppression,Phys. Rev. D104(2021) 074014

  34. [34]

    Beane, D.B

    S.R. Beane, D.B. Kaplan, N. Klco and M.J. Savage,Entanglement Suppression and Emergent Symmetries of Strong Interactions,Phys. Rev. Lett.122(2019) 102001

  35. [35]

    Collaboration,Observation of quantum entanglement with top quarks at the ATLAS detector,Nature633(2024) 542

    A. Collaboration,Observation of quantum entanglement with top quarks at the ATLAS detector,Nature633(2024) 542

  36. [36]

    E. Yazgan,Measurements of Top Quark Properties in CMS:t¯tSpin Density Matrix, Quantum Entanglement and Quantum Magic, inProceedings of The European Physical Society Conference on High Energy Physics — PoS(EPS-HEP2025), p. 273, Jan., 2026, DOI [2510.13743]

  37. [37]

    Florio, D

    A. Florio, D. Frenklakh, K. Ikeda, D. Kharzeev, V. Korepin, S. Shi et al.,Quantum real-time evolution of entanglement and hadronization in jet production: Lessons from the massive Schwinger model,Physical Review D110(2024) 094029

  38. [38]

    Florio, D

    A. Florio, D. Frenklakh, S. Grieninger, D.E. Kharzeev, A. Palermo and S. Shi,Thermalization from quantum entanglement: Jet simulations in the massive Schwinger model,Physical Review D112(2025) 094502

  39. [39]

    Y. Afik, F. Fabbri, M. Low, L. Marzola, J.A. Aguilar-Saavedra, M.M. Altakach et al.,Quantum Information Meets High-Energy Physics: Input to the Update of the European Strategy for Particle Physics,The European Physical Journal Plus140(2025) 855

  40. [40]

    Aoude, A.J

    R. Aoude, A.J. Barr, F. Maltoni and L. Satrioni,Decoherence Effects in Entangled Fermion Pairs at Colliders,Physical Review D113(2026) 076007

  41. [41]

    McGinnis,Symmetry, Entanglement, and the S-matrix,

    N. McGinnis,Symmetry, Entanglement, and the S-matrix,

  42. [42]

    McGinnis,Quantum Computational Structure ofSU(N)Scattering, Nov., 2025

    N. McGinnis,Quantum Computational Structure ofSU(N)Scattering, Nov., 2025. 10.48550/arXiv.2511.10550

  43. [43]

    Amram, L

    O. Amram, L. Anzalone, J. Birk, D.A. Faroughy, A. Hallin, G. Kasieczka et al.,Aspen Open Jets: unlocking LHC data for foundation models in particle physics,Mach. Learn. Sci. Tech.6 (2025) 030601 [2412.10504]

  44. [45]

    Guest, K

    D. Guest, K. Cranmer and D. Whiteson,Deep learning and its application to lhc physics, Annual Review of Nuclear and Particle Science(2018) [1806.11484]

  45. [46]

    de Oliveira, M

    L. de Oliveira, M. Kagan, L. Mackey, B. Nachman and A. Schwartzman,Jet-Images — Deep Learning Edition,Journal of High Energy Physics2016(2016) 69

  46. [47]

    Qu and L

    H. Qu and L. Gouskos,Jet tagging via particle clouds,Physical Review D101(2020)

  47. [48]

    Komiske, E.M

    P.T. Komiske, E.M. Metodiev and J. Thaler,Energy Flow Networks: Deep Sets for Particle Jets,Journal of High Energy Physics2019(2019) 121

  48. [49]

    Monk,Deep Learning as a Parton Shower,Journal of High Energy Physics2018(2018) 21

    J.W. Monk,Deep Learning as a Parton Shower,Journal of High Energy Physics2018(2018) 21. – 19 –

  49. [50]

    Y.S. Lai, D. Neill, M. Płoskoń and F. Ringer,Explainable machine learning of the underlying physics of high-energy particle collisions,Phys. Lett. B829(2022) 137055 [2012.06582]

  50. [51]

    Ghosh, X

    A. Ghosh, X. Ju, B. Nachman and A. Siodmok,Towards a deep learning model for hadronization,Physical Review D106(2022) 096020

  51. [52]

    J. Chan, X. Ju, A. Kania, B. Nachman, V. Sangli and A. Siodmok,Integrating Particle Flavor into Deep Learning Models for Hadronization,Physical Review D111(2025) 116015

  52. [53]

    J. Chan, X. Ju, A. Kania, B. Nachman, V. Sangli and A. Siodmok,Fitting a deep generative hadronization model,Journal of High Energy Physics(2023) [2305.17169]

  53. [54]

    Quantum algorithms for supervised and unsupervised machine learning

    S. Lloyd, M. Mohseni and P. Rebentrost,Quantum algorithms for supervised and unsupervised machine learning,1307.0411

  54. [55]

    X.-D. Cai, D. Wu, Z.-E. Su, M.-C. Chen, X.-L. Wang, L. Li et al.,Entanglement-Based Machine Learning on a Quantum Computer,Physical Review Letters114(2015) 110504

  55. [56]

    Schuld, I

    M. Schuld, I. Sinayskiy and F. Petruccione,Prediction by linear regression on a quantum computer,Physical Review A94(2016)

  56. [57]

    Schuld and F

    M. Schuld and F. Petruccione,Supervised Learning with Quantum Computers, vol. 17 of Quantum Science and Technology, Springer (2018)

  57. [58]

    Gibbs, Z

    J. Gibbs, Z. Holmes, M.C. Caro, N. Ezzell, H.-Y. Huang, L. Cincio et al.,Dynamical simulation via quantum machine learning with provable generalization,Physical Review Research6(2024) 013241

  58. [59]

    Belis, P

    V. Belis, P. Odagiu, M. Grossi, F. Reiter, G. Dissertori and S. Vallecorsa,Guided quantum compression for high dimensional data classification,Machine Learning: Science and Technology5(2024) 035010

  59. [60]

    Tüysüz,Quantum machine learning with near and future term quantum computers, Ph.D

    C. Tüysüz,Quantum machine learning with near and future term quantum computers, Ph.D. thesis, Humboldt U., Berlin, 2025. 10.18452/33365

  60. [61]

    Meyer, M

    J.J. Meyer, M. Mularski, E. Gil-Fuster, A.A. Mele, F. Arzani, A. Wilms et al.,Exploiting symmetry in variational quantum machine learning,PRX Quantum4(2023) 010328

  61. [62]

    Park and N

    C.-Y. Park and N. Killoran,Hamiltonian variational ansatz without barren plateaus,Quantum 8(2024) 1239

  62. [63]

    M.T. West, J. Heredge, M. Sevior and M. Usman,Provably Trainable Rotationally Equivariant Quantum Machine Learning,PRX Quantum5(2024) 030320

  63. [64]

    Marrero, B

    C.O. Marrero, B. Kiani and P.J. Coles,Expressivity of quantum neural networks through entanglement,PRX Quantum2(2021) 040316

  64. [65]

    X. Wang, Y. Chai, M. Demidik, X. Feng, K. Jansen and C. Tüysüz,Symmetry enhanced variational quantum imaginary time evolution, 2023

  65. [66]

    Campbell, M

    J.M. Campbell, M. Diefenthaler, T.J. Hobbs, S. Höche, J. Isaacson, F. Kling et al.,Event Generators for High-Energy Physics Experiments,SciPost Physics16(2024) 130

  66. [67]

    Khalek, S

    R.A. Khalek, S. Bailey, J. Gao, L. Harland-Lang and J. Rojo,Towards ultimate parton distributions at the high-luminosity LHC,The European Physical Journal C78(2018) 962. – 20 –

  67. [68]

    Abreu, W

    P. Abreu, W. Adam, T. Adye, E. Agasi, R. Aleksan, G.D. Alekseev et al.,Measurement of the Triple-Gluon Vertex from 4-Jet Events at LEP,Zeitschrift für Physik C Particles and Fields59 (1993) 357

  68. [69]

    Campbell, J.W

    J.M. Campbell, J.W. Huston and W.J. Stirling,Hard Interactions of Quarks and Gluons: A Primer for LHC Physics,Reports on Progress in Physics70(2006) 89

  69. [70]

    Collins, D.E

    J.C. Collins, D.E. Soper and G. Sterman,Soft gluons and factorization,Nucl. Phys. B308 (1988) 833

  70. [71]

    Peskin and D.V

    M.E. Peskin and D.V. Schroeder,An Introduction to Quantum Field Theory, Westview Press, Boulder, CO (1995)

  71. [72]

    Schwartz,Quantum Field Theory and the Standard Model, Cambridge University Press, Cambridge, UK (2014)

    M.D. Schwartz,Quantum Field Theory and the Standard Model, Cambridge University Press, Cambridge, UK (2014)

  72. [73]

    Afik and J.R

    Y. Afik and J.R. Muñoz de Nova,Quantum Information with Top Quarks in QCD,Quantum6 (2022) 820

  73. [74]

    Cheng, T

    K. Cheng, T. Han and M. Low,Quantum Tomography at Colliders: With or Without Decays,

  74. [75]

    10.48550/arXiv.2410.08303

  75. [76]

    Wootters,Entanglement of Formation of an Arbitrary State of Two Qubits,Phys

    W.K. Wootters,Entanglement of Formation of an Arbitrary State of Two Qubits,Phys. Rev. Lett.80(1998) 2245

  76. [77]

    Vidal,Efficient Classical Simulation of Slightly Entangled Quantum Computations,Physical Review Letters91(2003) 147902

    G. Vidal,Efficient Classical Simulation of Slightly Entangled Quantum Computations,Physical Review Letters91(2003) 147902

  77. [78]

    10.7483/OPENDATA.CMS.1KTG.X0W4

    CMS Collaboration,/JetHT/Run2016G-UL2016_MiniAODv2-v2/MINIAOD, 2024. 10.7483/OPENDATA.CMS.1KTG.X0W4

  78. [79]

    10.7483/OPENDATA.CMS.LT9E.T7RQ

    CMS Collaboration,/JetHT/Run2016H-UL2016_MiniAODv2-v2/MINIAOD, 2024. 10.7483/OPENDATA.CMS.LT9E.T7RQ

  79. [80]

    Larkoski, S

    A.J. Larkoski, S. Marzani, J. Thaler, A. Tripathee and W. Xue,Exposing the QCD Splitting Function with CMS Open Data,Phys. Rev. Lett.119(2017) 132003

  80. [81]

    The anti-k_t jet clustering algorithm

    M. Cacciari, G.P. Salam and G. Soyez,The anti-kt jet clustering algorithm,JHEP04(2008) 063 [0802.1189]

Showing first 80 references.