Paper Citation Record · LEDGER

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics

As of 10 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2604.15775.

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

pith.paper-citation-record.v1
2604.15775 v1

Coverage vector

measured 45 of 45 reference resolution

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

45 of 45 outbound references displayed

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External citation measurements

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Outbound references

Observation d0efe3b3-8a96-449b-9644-59c6459862d0 · outbound

This paper cites Cern data centre passes the 200-petabyte milestone.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Cern data centre passes the 200-petabyte milestone

Reference 1

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Observation b583fad9-1a67-451c-87cb-ff7ee350b3ef · outbound

This paper cites Machine learning in the search for new fundamental physics.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Machine learning in the search for new fundamental physics

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.118569Z

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Observation 89949fac-4e71-4a50-9d53-7faff91b6e59 · outbound

This paper cites High- energy nuclear physics meets machine learning.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics High- energy nuclear physics meets machine learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.123485Z

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Observation 8c0a448b-f263-4a35-81c1-98f5a94dcf85 · outbound

This paper cites Deep learning and its application to LHC physics.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Deep learning and its application to LHC physics

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.127832Z

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Observation 6bfa9d48-278d-4930-a26f-3f6e4ce97d90 · outbound

This paper cites The data-driven future of high- energy-density physics.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics The data-driven future of high- energy-density physics

Reference 5

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Observation a0eb4fe4-921f-4f32-a5f0-b61a54d2c3e2 · outbound

This paper cites Supervised learning with quantum- enhanced feature spaces.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Supervised learning with quantum- enhanced feature spaces

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.099704Z

Source-reported events for the cited work

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Observation dc1bcb40-392b-4cbf-a94e-f120290e3fe0 · outbound

This paper cites Quantum machine learning for chemistry and physics.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Quantum machine learning for chemistry and physics

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.108833Z

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Observation 24db8366-a7f5-40dd-957e-7836f02498fa · outbound

This paper cites Application of quantum machine learning using the quantum kernel algorithm to high energy physics analysis at the lhc us- ing ibm simulators and quantum hardware.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Application of quantum machine learning using the quantum kernel algorithm to high energy physics analysis at the lhc us- ing ibm simulators and quantum hardware

Reference 8

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Observation 2dbe24f8-d5f9-4579-ac30-ada3d0728a2d · outbound

This paper cites Event classification with quantum machine learning in high- energy physics.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Event classification with quantum machine learning in high- energy physics

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.106349Z

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Observation a75a8066-0388-41f7-830e-d5e6dbf5eea3 · outbound

This paper cites Quantum-inspired machine learning on high-energy physics data.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Quantum-inspired machine learning on high-energy physics data

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.111289Z

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Observation d897e225-3f03-4674-b9ee-952a73ba2239 · outbound

This paper cites A quantum machine learning- based predictive analysis of CERN collision events.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics A quantum machine learning- based predictive analysis of CERN collision events

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.066004Z

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Observation 426ad2b5-2753-41ce-9aed-3f973f6bab4a · outbound

This paper cites Ibm quantum roadmap 2025: Practical quantum computing era.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Ibm quantum roadmap 2025: Practical quantum computing era

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.051497Z

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Observation 19619366-b24c-4dbe-9766-6ba7f27beee4 · outbound

This paper cites The quantum echoes algorithm breakthrough.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics The quantum echoes algorithm breakthrough

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.046939Z

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Observation 1ed5558c-f47f-48a9-948d-72c903782562 · outbound

This paper cites Highly scalable quantum computing with neutral atoms.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Highly scalable quantum computing with neutral atoms

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.042105Z

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Observation b492cfea-c799-448b-8d9f-696fd6a04605 · outbound

This paper cites Pasqal releases 2025 roadmap showcasing upgradable archi- tecture toward fault-tolerant quantum computing.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Pasqal releases 2025 roadmap showcasing upgradable archi- tecture toward fault-tolerant quantum computing

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.039779Z

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Observation d25483ec-7539-406c-8ba3-70c11ee03b39 · outbound

This paper cites Ionq hits aq 64 milestone ahead of schedule.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Ionq hits aq 64 milestone ahead of schedule

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.075375Z

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Observation 34378b3e-cbba-4dc4-87b8-b0303c22acdc · outbound

This paper cites Quantum computing in the nisq era and beyond.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Quantum computing in the nisq era and beyond

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.034828Z

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Observation 1fd4511b-6eae-42a3-a4ce-045838fa279b · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Communication-efficient learning of deep networks from decentralized data

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.037112Z

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Observation c205b387-72e3-4ce3-81da-f565951409ad · outbound

This paper cites Advances and open problems in federated learning.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Advances and open problems in federated learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.029372Z

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Observation 3f4a7ef2-e569-4a48-9048-d5dc27d67fdd · outbound

This paper cites A privacy-preserving federated framework with hybrid quantum-enhanced learning for financial fraud detection.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics A privacy-preserving federated framework with hybrid quantum-enhanced learning for financial fraud detection

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.031581Z

Source-reported events for the cited work

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Observation 387afbcc-8460-4626-8215-b203d40f9236 · outbound

This paper cites FedQNN: Federated Learning using Quantum Neural Networks.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics FedQNN: Federated Learning using Quantum Neural Networks

Reference 21

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Source-reported events for the cited work

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Observation 1e0cf33b-d7a2-466e-9d36-d8377fc3c650 · outbound

This paper cites Federated quantum machine learning.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Federated quantum machine learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.116245Z

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Observation 22ca44de-026b-48b8-88c9-fb39b3a81e54 · outbound

This paper cites Searching for exotic particles in high-energy physics with deep learning.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Searching for exotic particles in high-energy physics with deep learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.044626Z

Source-reported events for the cited work

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Observation d45d32ce-15a1-42f1-bb3f-9db3427fbcf5 · outbound

This paper cites What is quantum computing and how it works.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics What is quantum computing and how it works

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.063913Z

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Observation 6b4afce4-8c9f-4199-ad78-df3ae2e3c455 · outbound

This paper cites Quantum machine learning.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Quantum machine learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.091940Z

Source-reported events for the cited work

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Observation b1c74c96-03f5-40e7-8aa5-ff31f65c0b3f · outbound

This paper cites A comprehensive review of data encoding techniques for quantum machine learning problems.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics A comprehensive review of data encoding techniques for quantum machine learning problems

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.087479Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation aebe4f3a-a849-44cc-a5d4-e7e4becbe2cb · outbound

This paper cites Quantum data encoding: a comparative analysis of classical-to-quantum mapping techniques and their impact on machine learning accuracy.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Quantum data encoding: a comparative analysis of classical-to-quantum mapping techniques and their impact on machine learning accuracy

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.089808Z

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Observation 9e2fc73d-e625-4730-a4bf-55afacd659d2 · outbound

This paper cites Comparative study of amplitude versus angle encoding in variational quantum classifiers.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Comparative study of amplitude versus angle encoding in variational quantum classifiers

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.094309Z

Source-reported events for the cited work

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Observation e003ff86-fb74-4b37-bd4e-acfe27440acf · outbound

This paper cites A repetitive amplitude encoding method for enhancing the mapping ability of quantum neural networks.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics A repetitive amplitude encoding method for enhancing the mapping ability of quantum neural networks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.102052Z

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Observation e70b7999-61eb-4e78-b074-011b40dd8330 · outbound

This paper cites Quantum angle encoding with learnable rotation applied to quantum machine learning.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Quantum angle encoding with learnable rotation applied to quantum machine learning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.113764Z

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Observation 22a62b71-62f6-4e69-86d4-a3dc10352fa0 · outbound

This paper cites Data re-uploading for a universal quantum classifier.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Data re-uploading for a universal quantum classifier

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.077551Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 20fa547e-c414-4142-9bc7-23f32a97bce2 · outbound

This paper cites Robust data encodings for quantum classifiers.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Robust data encodings for quantum classifiers

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.084886Z

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Observation 6ddcfa7a-f778-4a59-8a11-50eed9549da4 · outbound

This paper cites Expressive power of parametrized quantum circuits.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Expressive power of parametrized quantum circuits

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.082635Z

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Observation 9dda0fb2-ae05-438e-916b-c6573fb4128d · outbound

This paper cites On the practical usefulness of the hardware efficient ansatz.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics On the practical usefulness of the hardware efficient ansatz

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.104157Z

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Observation 7d822d93-5364-4898-b277-e5ff1576e399 · outbound

This paper cites Quantum long short-term memory (qlstm) vs. classical lstm: A comparative analysis for solar power forecasting.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Quantum long short-term memory (qlstm) vs. classical lstm: A comparative analysis for solar power forecasting

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.068361Z

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Observation c6a4f3ba-5c96-41fb-a2f6-f8348346b524 · outbound

This paper cites Quantum long short-term memory.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Quantum long short-term memory

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.072872Z

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Observation 5019cda0-621d-4087-8ea4-7ea0092fcd59 · outbound

This paper cites Quantum federated learning through blind quantum computing.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Quantum federated learning through blind quantum computing

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.049241Z

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Observation 7a47c9d9-1796-4724-a3c9-8fafef727e84 · outbound

This paper cites Quantum federated learning with quantum data.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Quantum federated learning with quantum data

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.097147Z

Source-reported events for the cited work

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Observation 760a6798-e3d5-4d4a-9e6f-a955e67906c0 · outbound

This paper cites Data preservation in high energy physics.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Data preservation in high energy physics

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.056629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 88e40f49-dd87-4ea9-8913-4e2db6f76c74 · outbound

This paper cites Heterogeneous Federated Learning: State-of-the-art and Research Challenges.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Heterogeneous Federated Learning: State-of-the-art and Research Challenges

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.059104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 588733dd-19aa-41fb-bb19-8891ed292520 · outbound

This paper cites SUSY , the Third Generation and the LHC.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics SUSY , the Third Generation and the LHC

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.070568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7f63b4e2-df40-414b-85cc-4828ffbe457b · outbound

This paper cites Whiteson, “SUSY.” UCI Machine Learning Repository.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Whiteson, “SUSY.” UCI Machine Learning Repository

Reference 42

Resolution: verified exact; doi, observed 2026-05-10T09:18:30.765885Z

Source-reported events for the cited work

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Observation 5e2ffbcf-8687-4586-9994-147bd37ab4ab · outbound

This paper cites Simulating Quantum Computations on Classical Machines: A Survey.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Simulating Quantum Computations on Classical Machines: A Survey

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:18:31.383170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d4d8b60b-3fa4-4b5d-bcf5-8985faf04421 · outbound

This paper cites Client Selection in Federated Learning: Principles, Challenges, and Opportunities.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Client Selection in Federated Learning: Principles, Challenges, and Opportunities

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.061526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 80f5efbf-3de3-406c-aec1-bca261937231 · outbound

This paper cites Efficient client contribution evaluation for horizontal federated learning.

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics Efficient client contribution evaluation for horizontal federated learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T17:43:49.054267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.