Paper Citation Record · LEDGER

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics

As of 10 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 1 inbound Pith citation observation for arXiv:2511.15672.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2511.15672 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

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measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T15:18:43.491499Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

52 of 52 outbound references displayed

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External citation measurements

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Outbound references

Observation eaab566c-8276-4bcf-832c-6b96099c76a8 · outbound

This paper cites Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC

Reference 1

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Observation e010e91c-6c52-4da6-af42-860ddbda9b5d · outbound

This paper cites Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T21:24:02.944049Z

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Observation a18e79a8-4ded-4c9b-9185-78fd3fc608aa · outbound

This paper cites Combined measurement of the Higgs boson mass from the $H\to\gamma\gamma$ and $H\to ZZ^{*} \to 4\ell$ decay channels with the ATLAS detector using $\sqrt{s}$ = 7, 8 and 13 TeV $pp$ collision data.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Combined measurement of the Higgs boson mass from the $H\to\gamma\gamma$ and $H\to ZZ^{*} \to 4\ell$ decay channels with the ATLAS detector using $\sqrt{s}$ = 7, 8 and 13 TeV $pp$ collision data

Reference 3

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Observation b5c447fa-03e3-49de-bb1c-28086d48f9ee · outbound

This paper cites Measurement of the Higgs boson mass and width using the four-lepton final state in proton-proton collisions at $\sqrt{s}$ = 13 TeV.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Measurement of the Higgs boson mass and width using the four-lepton final state in proton-proton collisions at $\sqrt{s}$ = 13 TeV

Reference 4

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Observation 6af62d6f-a2e5-4aef-b518-57c6ca789ddf · outbound

This paper cites an unresolved cited work.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Unresolved cited work

Reference 5

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Observation 2f644936-1637-496d-9208-6cde0b7b2bf8 · outbound

This paper cites Constraints on the Higgs boson self-coupling from single- and double-Higgs production with the ATLAS detector using $pp$ collisions at $\sqrt{s}=13$ TeV.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Constraints on the Higgs boson self-coupling from single- and double-Higgs production with the ATLAS detector using $pp$ collisions at $\sqrt{s}=13$ TeV

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T21:24:03.270653Z

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Observation 9a48a0da-d8e2-4cc0-ae0e-93980ed4e06b · outbound

This paper cites Constraints on the Higgs boson self-coupling from the combination of single and double Higgs boson production in proton-proton collisions at $\sqrt{s}$ = 13 TeV.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Constraints on the Higgs boson self-coupling from the combination of single and double Higgs boson production in proton-proton collisions at $\sqrt{s}$ = 13 TeV

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T21:24:03.334317Z

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Observation 8606743e-31d5-4aed-9e9f-a50d1107df44 · outbound

This paper cites Handbook of LHC Higgs Cross Sections: 4. Deciphering the Nature of the Higgs Sector.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Handbook of LHC Higgs Cross Sections: 4. Deciphering the Nature of the Higgs Sector

Reference 8

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Observation 3db19e24-a2d1-4192-8040-836ca40e14b6 · outbound

This paper cites Study of Higgs boson pair production in the $HH \rightarrow b \overline{b} \gamma \gamma$ final state with 308 fb$^{-1}$ of data collected at $\sqrt{s} =$ 13 TeV and 13.6 TeV by the ATLAS experiment.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Study of Higgs boson pair production in the $HH \rightarrow b \overline{b} \gamma \gamma$ final state with 308 fb$^{-1}$ of data collected at $\sqrt{s} =$ 13 TeV and 13.6 TeV by the ATLAS experiment

Reference 9

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Observation 80a9f2a8-b0f4-4ce7-a72e-7a8f0d5f82c2 · outbound

This paper cites an unresolved cited work.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Unresolved cited work

Reference 10

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Observation a397eb73-5302-407c-a287-c96b4b13708f · outbound

This paper cites Novel machine learning applications at the LHC.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Novel machine learning applications at the LHC

Reference 11

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Observation 4ff4f1f5-76e8-4070-8823-5f07a271bccc · outbound

This paper cites Belfkir, M.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Belfkir, M

Reference 12

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Observation 2b37f710-0103-47c3-a6ff-b83f6f46a72a · outbound

This paper cites Quantum Machine Learning in High Energy Physics.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Quantum Machine Learning in High Energy Physics

Reference 13

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Observation 5ae9cabd-ac7e-45b7-81a1-7aedb1a69378 · outbound

This paper cites Supervised learning with quantum enhanced feature spaces.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Supervised learning with quantum enhanced feature spaces

Reference 14

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Observation 5ea7fd30-7099-4cae-8d42-d6adf579fff5 · outbound

This paper cites Circuit-centric quantum classifiers.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Circuit-centric quantum classifiers

Reference 15

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Observation 998d7391-464f-40a6-bcd5-62502c62641d · outbound

This paper cites Supplementary information for "Quantum supremacy using a programmable superconducting processor".

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Supplementary information for "Quantum supremacy using a programmable superconducting processor"

Reference 16

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Observation bed1019f-07bd-4ff6-a4c3-8eafd2a047da · outbound

This paper cites Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC

Reference 17

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Observation c1f806e2-cda1-43f7-a4a7-dc5cb0d97aea · outbound

This paper cites Application of Quantum Machine Learning in a Higgs Physics Study at the CEPC.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Application of Quantum Machine Learning in a Higgs Physics Study at the CEPC

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T21:24:04.777500Z

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Observation 58b1723a-fd1e-438e-ae6c-deb70a811ff7 · outbound

This paper cites Precise predictions for double-Higgs production via vector-boson fusion.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Precise predictions for double-Higgs production via vector-boson fusion

Reference 19

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Observation 8d5bdd1a-d3a5-4c4d-8f3f-ac0f67e2fb3e · outbound

This paper cites Vector-Boson Fusion Higgs Pair Production at N$^3$LO.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Vector-Boson Fusion Higgs Pair Production at N$^3$LO

Reference 20

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Observation 37a4d5d9-dae7-408a-b0ee-b5b993818aaf · outbound

This paper cites Theory and phenomenology of two-Higgs-doublet models.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Theory and phenomenology of two-Higgs-doublet models

Reference 21

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Observation 96cce4f7-ebe9-43df-8d91-49d41af4e602 · outbound

This paper cites The Next-to-Minimal Supersymmetric Standard Model.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics The Next-to-Minimal Supersymmetric Standard Model

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T21:24:05.435196Z

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Observation b23fe9a0-efdb-4b28-84f9-84ae9be2f6b9 · outbound

This paper cites Two-real-scalar-singlet extension of the SM: LHC phenomenology and benchmark scenarios.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Two-real-scalar-singlet extension of the SM: LHC phenomenology and benchmark scenarios

Reference 23

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Observation 4646d4f5-d6a2-49f3-96ff-e46ba64c436c · outbound

This paper cites Higgs boson pair production at NNLO with top quark mass effects.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Higgs boson pair production at NNLO with top quark mass effects

Reference 24

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Observation ac3a4dec-8ec3-4226-9d92-c97a70624436 · outbound

This paper cites A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX

Reference 25

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Observation 1d94c940-04d5-47bd-a754-211f99675cd0 · outbound

This paper cites The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T21:24:05.897284Z

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Observation b17e4745-97ce-4f69-9591-eeea0eb3ef91 · outbound

This paper cites A comprehensive guide to the physics and usage of PYTHIA 8.3.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics A comprehensive guide to the physics and usage of PYTHIA 8.3

Reference 27

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Observation 523c6dfc-fe2d-4c98-aa17-2659b01139a7 · outbound

This paper cites DELPHES 3, A modular framework for fast simulation of a generic collider experiment.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics DELPHES 3, A modular framework for fast simulation of a generic collider experiment

Reference 28

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Observation 60be878d-4b7b-440c-9ea3-63730bb42992 · outbound

This paper cites Electron and photon performance measurements with the ATLAS detector using the 2015-2017 LHC proton-proton collision data.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Electron and photon performance measurements with the ATLAS detector using the 2015-2017 LHC proton-proton collision data

Reference 29

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Observation f8da659a-fedf-487f-beb5-448984b81da8 · outbound

This paper cites Aadet al.(ATLAS), Measurement of photon identification efficiency using radiative z decays using 2022-2024 collision data at the atlas experiment ().

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Aadet al.(ATLAS), Measurement of photon identification efficiency using radiative z decays using 2022-2024 collision data at the atlas experiment ()

Reference 30

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Observation f4ea52bf-6ac5-4343-a276-b908560b6b0a · outbound

This paper cites The anti-k_t jet clustering algorithm.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics The anti-k_t jet clustering algorithm

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T21:24:06.439563Z

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Observation e530aa9a-41b4-46d9-aca5-28de50606088 · outbound

This paper cites FastJet user manual.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics FastJet user manual

Reference 32

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Observation 3bd2983f-088b-48b9-937e-00eca3c22692 · outbound

This paper cites Aadet al.(ATLAS), Transforming jet flavour tagging at ATLAS, CERN-EP-2025-103 (2025), arXiv:2505.19689 [hep-ex].

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Aadet al.(ATLAS), Transforming jet flavour tagging at ATLAS, CERN-EP-2025-103 (2025), arXiv:2505.19689 [hep-ex]

Reference 33

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This paper cites Aadet al.(ATLAS), The atlas trigger system for lhc run 3 and trigger performance in 2022 ().

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Aadet al.(ATLAS), The atlas trigger system for lhc run 3 and trigger performance in 2022 ()

Reference 34

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Observation 709744d9-8a4e-44ea-9bd2-e952c62ba4b3 · outbound

This paper cites Studies of new Higgs boson interactions through nonresonant $HH$ production in the $b\bar{b}\gamma\gamma$ final state in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Studies of new Higgs boson interactions through nonresonant $HH$ production in the $b\bar{b}\gamma\gamma$ final state in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector

Reference 35

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Observation a016c02f-fd8f-4eb1-9128-4c4d669ab947 · outbound

This paper cites Search for Higgs boson pair production in the two bottom quarks plus two photons final state in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Search for Higgs boson pair production in the two bottom quarks plus two photons final state in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector

Reference 36

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Observation 5513a583-10b8-4b4a-a3b7-7a91fca793bc · outbound

This paper cites Measurement of the Higgs boson mass from the $H\rightarrow \gamma\gamma$ and $H \rightarrow ZZ^{*} \rightarrow 4\ell$ channels with the ATLAS detector using 25 fb$^{-1}$ of $pp$ collision data.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Measurement of the Higgs boson mass from the $H\rightarrow \gamma\gamma$ and $H \rightarrow ZZ^{*} \rightarrow 4\ell$ channels with the ATLAS detector using 25 fb$^{-1}$ of $pp$ collision data

Reference 37

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Observation 922e37a1-f09a-442e-849c-d0927e56ac68 · outbound

This paper cites On the impact of selected modern deep-learning techniques to the performance and celerity of classification models in an experimental high-energy physics use case.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics On the impact of selected modern deep-learning techniques to the performance and celerity of classification models in an experimental high-energy physics use case

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-03T21:24:07.357584Z

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Observation 280c12b7-b17a-443e-a679-6e44979eba64 · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-03T21:24:07.478833Z

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Observation 6fcead0a-b7e3-402f-8ea8-fceb2c81c0dc · outbound

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From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Pedregosa, G

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-03T21:24:07.624834Z

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Observation dc7420cd-59c9-41df-9163-28dda9666237 · outbound

This paper cites Sculpting Quantum Landscapes: Fubini-Study Metric Conditioning for Geometry Aware Learning in Parameterized Quantum Circuits.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Sculpting Quantum Landscapes: Fubini-Study Metric Conditioning for Geometry Aware Learning in Parameterized Quantum Circuits

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-03T21:24:07.740306Z

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Observation dbc26cbe-1976-4b36-b66a-e6fd07513dec · outbound

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From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Haug and M

Reference 42

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Observation 7360b948-68be-4382-85cf-1d9729cc59ff · outbound

This paper cites PennyLane: Automatic differentiation of hybrid quantum-classical computations.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics PennyLane: Automatic differentiation of hybrid quantum-classical computations

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-03T21:24:08.017454Z

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This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-03T21:24:08.181377Z

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Observation 9cdc87b8-6f41-433a-8439-07a82f650f65 · outbound

This paper cites Deep Learning using Rectified Linear Units (ReLU).

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Deep Learning using Rectified Linear Units (ReLU)

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-03T21:24:08.300419Z

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Observation ec0544e1-c3fd-4c78-8f7e-306a3e840d88 · outbound

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From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Quantum circuits of CNOT gates

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-03T21:24:08.487186Z

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Observation b056c3e0-e869-4e72-9bf3-de306778ae67 · outbound

This paper cites Hospedales, A.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Hospedales, A

Reference 47

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Observation e0f442cc-e8a7-4639-9e79-5288effdaa55 · outbound

This paper cites Barren Plateaus in Variational Quantum Computing.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Barren Plateaus in Variational Quantum Computing

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-03T21:24:08.704792Z

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Observation 5511de3a-403c-4c04-9569-34d6ed2089a2 · outbound

This paper cites Ma´ ckiewicz and W.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Ma´ ckiewicz and W

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-03T21:24:08.745442Z

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Observation 80360205-4f81-4323-ae12-c5d6a00d262e · outbound

This paper cites Asymptotic formulae for likelihood-based tests of new physics.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Asymptotic formulae for likelihood-based tests of new physics

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-03T21:24:08.802162Z

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Observation 5907b075-1ed0-4faa-8a43-1412bfbaf9d4 · outbound

This paper cites an unresolved cited work.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics Unresolved cited work

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-03T21:24:08.883598Z

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Observation adc71410-2def-439f-8a48-025fee4f09be · outbound

This paper cites pyhf: pure-Python implementation of HistFactory with tensors and automatic differentiation.

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics pyhf: pure-Python implementation of HistFactory with tensors and automatic differentiation

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-03T21:24:08.961240Z

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Pith citing papers

Observation 84bf0f7b-0958-47d2-8e04-35d004393f93 · inbound

Probing new physics in the Boosted $HH \to b\bar{b}\gamma\gamma$ channel at the LHC cites this paper.

Probing new physics in the Boosted $HH \to b\bar{b}\gamma\gamma$ channel at the LHC From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics

Reference 42

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