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Paper Citation Record · LEDGER

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids

As of 14 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2411.09123.

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

pith.paper-citation-record.v1
2411.09123 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:08:21.123132Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T03:52:17.171544Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

31 of 31 outbound references displayed

  • verified exact0
  • verified fuzzy12
  • unresolved18
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 4e94e2de-db9a-483c-80bf-442ecd5a2256 · outbound

This paper cites That is, perform the transformation |0⟩nb 7→ |b⟩nb.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids That is, perform the transformation |0⟩nb 7→ |b⟩nb

Reference 1

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T21:08:20.983280Z digest=sha256:ace5fbbcc715c522f43e4afa33ce2a51ea8017a14cc82de51d985fdbf0a54e5c

Observation e74f5ec3-6867-4412-8f2c-28902bdf2c02 · outbound

This paper cites an unresolved cited work.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Unresolved cited work

Reference 2

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source=pdf_text observed=2026-08-12T21:08:20.978185Z digest=sha256:7c1e94ac60bd57eeff6c2b00a95283b90d74a26333afa20fd2f7b07a39213041

Observation e5a0aa27-d3f0-46a4-bd55-db1ee9858bc9 · outbound

This paper cites an unresolved cited work.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Unresolved cited work

Reference 3

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source=pdf_text observed=2026-08-12T21:08:20.992772Z digest=sha256:18c9ed169aecb7079451714017dcd121505a0fa9f15ba2cfe9b82cce65f6c8eb

Observation 75f19f65-a339-4814-a682-ffa644e507ff · outbound

This paper cites (13) The quantum state of the register expressed in the eigenbasis of A is now N −1X j=0 bj|λj⟩nl |uj⟩nb , (14) where |λj⟩nl is the nl-bit binary representation of λj.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids (13) The quantum state of the register expressed in the eigenbasis of A is now N −1X j=0 bj|λj⟩nl |uj⟩nb , (14) where |λj⟩nl is the nl-bit binary representation of λj

Reference 4

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source=pdf_text observed=2026-08-12T21:08:20.987186Z digest=sha256:376999e3ffdda385ae6771b4f8015e9e86f5617f48f3cf478870a738569ecf6e

Observation 67019e40-3483-4060-b5b1-2ce6528fbc4d · outbound

This paper cites an unresolved cited work.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Unresolved cited work

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T21:08:21.002974Z digest=sha256:adc782a9e660ae40df285ea906e8abfae7a7e84572b1175a5cf28e06b0499f9d

Observation 17dc757b-c5fc-4eba-a88d-5743a1d9c823 · outbound

This paper cites Ignoring possible errors from QPE, this results in N −1X j=0 bj|0⟩nl |uj⟩nb s 1 − C 2 λ2 j |0⟩ + C λj |1⟩ !.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Ignoring possible errors from QPE, this results in N −1X j=0 bj|0⟩nl |uj⟩nb s 1 − C 2 λ2 j |0⟩ + C λj |1⟩ !

Reference 6

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source=pdf_text observed=2026-08-12T21:08:20.997793Z digest=sha256:a192e7d55a388cd2ee4e5a3e113695f8c6d11377645e0197263010351c1025d9

Observation 1bbca6da-1e11-412a-baea-ad10c1ed383f · outbound

This paper cites Dahale and B.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Dahale and B

Reference 7

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source=pdf_text observed=2026-08-12T21:08:21.045319Z digest=sha256:c25fdce3f8e41e679cf35559eb05faea77e02ae25636354ffe4b25f1543eb6c9

Observation 105f4595-c599-4583-8c03-d4d8342074e3 · outbound

This paper cites an unresolved cited work.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Unresolved cited work

Reference 8

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source=pdf_text observed=2026-08-12T21:08:21.006463Z digest=sha256:2004c999024123e0a96e5797e9723df36aca6cef9816ff8dcb0b1c2c96a44387

Observation ced89e8c-06b3-4990-b8b4-a65c0d419e12 · outbound

This paper cites Rasmussen and C.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Rasmussen and C

Reference 9

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source=pdf_text observed=2026-08-12T21:08:21.014453Z digest=sha256:4bc3ad460ce5ff731ba27d115a5df18ad32c5151a09f1f95c1292ae286e3dd77

Observation c391fa34-6737-42f0-b030-0d841b2c6889 · outbound

This paper cites an unresolved cited work.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Unresolved cited work

Reference 10

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source=pdf_text observed=2026-08-12T21:08:21.019984Z digest=sha256:33ae888e149c097b3ec70890e2b1d5db1ee1488dba8d431aa0e1b1d43dd675f1

Observation 5443694c-2ac2-4593-9389-afc957a6cfc1 · outbound

This paper cites Jalali, V.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Jalali, V

Reference 11

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source=pdf_text observed=2026-08-12T21:08:21.023183Z digest=sha256:e5e2935c18702281ed9faba22745452823cf2244b52f85da05e4502360b34837

Observation 1f13bbdd-aef5-440b-aa75-1c53e2d4051b · outbound

This paper cites an unresolved cited work.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Unresolved cited work

Reference 12

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source=pdf_text observed=2026-08-12T21:08:21.029947Z digest=sha256:51e1d2cb87ec9a74be5e18f6a294068c1b39dec440c0a7dfa04c3ee0867d3dbc

Observation cac22fb4-2c48-468d-a09b-e5959e0fb6a4 · outbound

This paper cites an unresolved cited work.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Unresolved cited work

Reference 13

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T21:08:21.034453Z digest=sha256:bb45a401c4caea3d69851c4d8b50b390a6a8c64c39172e18b91a9572fbeba801

Observation f3f4d56e-780a-426a-88ad-7a4390483a10 · outbound

This paper cites Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids

Reference 14

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source=pdf_text observed=2026-08-12T21:08:21.039244Z digest=sha256:e1b87383db654d92263a203b193e115adb7e396f385a3fa4629722f39e740181

Observation d0008f6a-52ea-49cf-bdc4-aef019ce4a48 · outbound

This paper cites an unresolved cited work.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Unresolved cited work

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T21:08:21.079625Z digest=sha256:f5e5a04f5628351930bca022100f1e08acf4781d761f1f7732a95f91cec39fa4

Observation 6efead34-f1f9-4123-9c76-1be7c1aa6fe5 · outbound

This paper cites The rest of the article is organized into five main sec- tions.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids The rest of the article is organized into five main sec- tions

Reference 16

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source=pdf_text observed=2026-08-12T21:08:20.969956Z digest=sha256:5c86d43ac0affce4c12ea60f16db2f18a542ade083ba6daf4911e326967b5da5

Observation beccd4b6-2e17-4529-8ec5-1563c8f77361 · outbound

This paper cites an unresolved cited work.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Unresolved cited work

Reference 17

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source=pdf_text observed=2026-08-12T21:08:21.050009Z digest=sha256:89efe485b63c466989f00702408d4c91f50d0bed6701e879de66a750c70a2fa4

Observation 36b8418b-4ece-440d-b195-dcae8b39c12c · outbound

This paper cites an unresolved cited work.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Unresolved cited work

Reference 18

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source=pdf_text observed=2026-08-12T21:08:21.056066Z digest=sha256:bec8ad283fad01309ef174f4592f639a174fe5ed0c5b51d6d2d6199ae266742b

Observation f20a5256-ef3a-4018-b9dc-70c528e7f38f · outbound

This paper cites an unresolved cited work.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Unresolved cited work

Reference 19

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source=pdf_text observed=2026-08-12T21:08:21.062243Z digest=sha256:29e0b505d973f5f0305fc44fc6ab461f212e645131b962ded0f93ce41dc71dd8

Observation 2fe2fc51-612d-43b3-a7b0-9e85e213b9c4 · outbound

This paper cites Quantum walk speedup of backtracking algorithms,.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Quantum walk speedup of backtracking algorithms,

Reference 20

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T21:08:21.066171Z digest=sha256:de8a1dbe252ed0cd05cb45403ec85bd687d73248608387183b18d314ac195ba7

Observation ecdac1fb-dd66-4beb-90d2-c085c18ac046 · outbound

This paper cites Quantum al- gorithms for supervised and unsupervised machine learn- ing,.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Quantum al- gorithms for supervised and unsupervised machine learn- ing,

Reference 21

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source=pdf_text observed=2026-08-12T21:08:21.069569Z digest=sha256:bd2dbdcd06536afc5205991afce8d668f1d7a64aaaddf1d5735f5fa4eb698480

Observation 548b3ba7-a819-45ca-bc7a-31bbf54d2ad8 · outbound

This paper cites an unresolved cited work.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Unresolved cited work

Reference 22

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source=pdf_text observed=2026-08-12T21:08:21.073167Z digest=sha256:7e62dbc40b4c2f7e4c659fa53b833396d704aedf5da5ce3c0c7f0cf0729b30c4

Observation 337a3a5c-7bb3-46da-9e2e-891caf4042ac · outbound

This paper cites Rebentrost, M.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Rebentrost, M

Reference 23

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T21:08:21.076493Z digest=sha256:ddb48cb90b7d3e39bccca3622e6bda3ecda9ac18375b396ab89cf0e5fefcb3dc

Observation 9dcc4460-8567-4dca-bbfd-91c81c5f52ed · outbound

This paper cites Quantum Computing for Power Flow Algorithms: Testing on real Quantum Computers.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Quantum Computing for Power Flow Algorithms: Testing on real Quantum Computers

Reference 24

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source=pdf_text observed=2026-08-12T21:08:21.089443Z digest=sha256:bcdbaa88519230cbc730ac0665b5c000fcda2fb6e35ad0c2da382a342bd87c96

Observation a0e79e1c-2ce2-4c90-bc66-3f500f874be8 · outbound

This paper cites an unresolved cited work.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Unresolved cited work

Reference 25

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source=pdf_text observed=2026-08-12T21:08:21.096790Z digest=sha256:7d2966ddc657b352aa3edc91e6942e0ef5de4ec29ec5fe64e00c46ec67ed5451

Observation 7e8a91fc-a97c-4725-8d2f-d4d2e47d9ede · outbound

This paper cites Abur and A.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Abur and A

Reference 26

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source=pdf_text observed=2026-08-12T21:08:21.101167Z digest=sha256:2c580cf66219bfe61234a24108cfd6bfd72d63eabae059b2ca033128e89631a5

Observation 8c80e851-63a3-4e39-9e41-98cb36839fd9 · outbound

This paper cites Thurner, A.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Thurner, A

Reference 27

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source=pdf_text observed=2026-08-12T21:08:21.106387Z digest=sha256:9b154971a8ff8b61329141b384b587910f1703b0b2f1df4b88aca51c9786e040

Observation 2c74e299-5dfe-46d9-b99a-bc4bac59e630 · outbound

This paper cites an unresolved cited work.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Unresolved cited work

Reference 28

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source=pdf_text observed=2026-08-12T21:08:21.110861Z digest=sha256:6e3652d5f5d3de0b83ca6a5bab7e4f19c406c46449a576117b285937e0fcf09f

Observation 9f55c168-76be-46df-a30e-baad5d3644be · outbound

This paper cites an unresolved cited work.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Unresolved cited work

Reference 29

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source=pdf_text observed=2026-08-12T21:08:21.115083Z digest=sha256:62aa0898caaf1e8ba9553cbbbe79f46e218d9b54fd13154d826a2c28a2e8f3d5

Observation a6e08ee3-d5c6-40f1-99c6-4162ecaedc32 · outbound

This paper cites Madden and A.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Madden and A

Reference 30

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source=pdf_text observed=2026-08-12T21:08:21.119776Z digest=sha256:4511a57a7029e1e28b938e0105aa8bfd4976cc0c7accba27d5e09305d00e3534

Observation afdddb10-acd5-4af5-aa71-e8aea8937071 · outbound

This paper cites Ghosh, K.

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Ghosh, K

Reference 31

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source=pdf_text observed=2026-08-12T21:08:21.123132Z digest=sha256:6eb90baeca232fb5a2f098e619d22ea125ac6559f550fa5448ed892fed7df66d

Pith citing papers

Observation 5f10dc08-2655-4cb7-863d-ca0c69481131 · inbound

Time Evolution on Hybrid Tensor Networks -- A Novel and Parallelizable Algorithm cites this paper.

Time Evolution on Hybrid Tensor Networks -- A Novel and Parallelizable Algorithm Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids

Reference 42

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arxiv_id, observed 2026-06-29T03:53:04.583337Z

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source=arxiv_source observed=2026-06-29T03:52:17.171544Z digest=sha256:ebf2ea1044871d402eef6ba9895d7e4352ad04319f11154133ece211603a4a23