Paper Citation Record · LEDGER

Quantum Annealing based Feature Selection in Machine Learning

As of 12 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2411.19609.

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

pith.paper-citation-record.v1
2411.19609 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T06:06:22.548129Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

47 of 47 outbound references displayed

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External citation measurements

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Outbound references

Observation a7774dd5-c284-4133-9635-e05074e52bf4 · outbound

This paper cites Quantum machine learning in feature Hilbert spaces.

Quantum Annealing based Feature Selection in Machine Learning Quantum machine learning in feature Hilbert spaces

Reference 1

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Observation 9c8997c5-0f40-4fb0-9230-f1d9a1a94cc5 · outbound

This paper cites Quantum machine learning.

Quantum Annealing based Feature Selection in Machine Learning Quantum machine learning

Reference 2

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Observation d3c827d5-70cc-41d6-9f1c-a0c5cba3c186 · outbound

This paper cites Unknown Element of Owning Costs - Impact of Residual Value.

Quantum Annealing based Feature Selection in Machine Learning Unknown Element of Owning Costs - Impact of Residual Value

Reference 3

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Observation 7753514a-dbce-44c5-bc03-3a495cc4905d · outbound

This paper cites CASH-FLOW AND RESIDUAL V ALUE ANALYSIS FOR CONSTRUCTION EQUIP- MENT.

Quantum Annealing based Feature Selection in Machine Learning CASH-FLOW AND RESIDUAL V ALUE ANALYSIS FOR CONSTRUCTION EQUIP- MENT

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:23.185512Z

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Observation ba01c26d-b96f-43a8-a2ad-13090ca2879c · outbound

This paper cites Maintenance cost and residual value predic- tion of heavy construction equipment.

Quantum Annealing based Feature Selection in Machine Learning Maintenance cost and residual value predic- tion of heavy construction equipment

Reference 5

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Observation 1ff7e23a-e376-441c-9df8-f0d1679e02bb · outbound

This paper cites Estimating residual value of heavy construction equipment using ensemble learning.

Quantum Annealing based Feature Selection in Machine Learning Estimating residual value of heavy construction equipment using ensemble learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:23.163487Z

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Observation 033b697c-2fac-4084-9922-2215ab329f09 · outbound

This paper cites Machine learning models for predict- ing the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost regression.

Quantum Annealing based Feature Selection in Machine Learning Machine learning models for predict- ing the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost regression

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:23.149883Z

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Observation e95a9315-d377-4f8b-ade9-0a040b942cd0 · outbound

This paper cites Deep and machine learning ap- proaches for forecasting the residual value of heavy construction equipment: a management decision support model.

Quantum Annealing based Feature Selection in Machine Learning Deep and machine learning ap- proaches for forecasting the residual value of heavy construction equipment: a management decision support model

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:23.137810Z

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Observation 9613a40b-86bf-4bb6-8791-7a18cbaebb6c · outbound

This paper cites Benchmarking Automated Machine Learning Methods for Price Forecasting Applications.

Quantum Annealing based Feature Selection in Machine Learning Benchmarking Automated Machine Learning Methods for Price Forecasting Applications

Reference 9

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Observation 00897eee-1bd4-45d6-91fd-c9ee459d3904 · outbound

This paper cites Incremental search space construction for ma- chine learning pipeline synthesis.

Quantum Annealing based Feature Selection in Machine Learning Incremental search space construction for ma- chine learning pipeline synthesis

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:23.126823Z

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Observation 7381b4f2-6f2f-4026-b79b-ace7b433044e · outbound

This paper cites Neural Architecture Search with Reinforcement Learning.

Quantum Annealing based Feature Selection in Machine Learning Neural Architecture Search with Reinforcement Learning

Reference 11

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Source-reported events for the cited work

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Observation 44064535-c01f-44ee-a69f-779a4e21cd1f · outbound

This paper cites Evaluating Quantum Support Vector Regression Meth- ods for Price Forecasting Applications.

Quantum Annealing based Feature Selection in Machine Learning Evaluating Quantum Support Vector Regression Meth- ods for Price Forecasting Applications

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:23.115804Z

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Observation e432849f-1703-4e2a-8a8a-8486c4b2f212 · outbound

This paper cites Support vector machines.

Quantum Annealing based Feature Selection in Machine Learning Support vector machines

Reference 13

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Observation 8f5b6ff5-9cc1-4934-a133-4c20e8408af4 · outbound

This paper cites Power of data in quantum machine learning.

Quantum Annealing based Feature Selection in Machine Learning Power of data in quantum machine learning

Reference 14

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Observation 49bf1d23-8e2e-46b1-aaa0-9a86edeba08a · outbound

This paper cites Exponential concentration in quantum kernel methods.

Quantum Annealing based Feature Selection in Machine Learning Exponential concentration in quantum kernel methods

Reference 15

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Observation a5350f64-147a-42bd-b0f4-5a0544eb47f0 · outbound

This paper cites Quantum annealing: A new method for minimizing multidimensional functions.

Quantum Annealing based Feature Selection in Machine Learning Quantum annealing: A new method for minimizing multidimensional functions

Reference 16

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Observation 60a699a4-d4ba-444e-887a-14c0a20032d3 · outbound

This paper cites QUBO formulations for training machine learning models.

Quantum Annealing based Feature Selection in Machine Learning QUBO formulations for training machine learning models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:23.077726Z

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Observation 12d9662e-8523-40e3-8ddd-d784aa2903ff · outbound

This paper cites Support vector machines on the D- Wave quantum annealer.

Quantum Annealing based Feature Selection in Machine Learning Support vector machines on the D- Wave quantum annealer

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:23.067071Z

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Observation cd9c1000-ce85-42aa-8b27-25c3770627de · outbound

This paper cites Mixed Quantum–Classical Method for Fraud Detection With Quantum Feature Se- lection.

Quantum Annealing based Feature Selection in Machine Learning Mixed Quantum–Classical Method for Fraud Detection With Quantum Feature Se- lection

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:23.056424Z

Source-reported events for the cited work

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Observation 78b435b2-9a98-4d60-b155-b0f2c81433dd · outbound

This paper cites A re- view of feature selection methods with applications.

Quantum Annealing based Feature Selection in Machine Learning A re- view of feature selection methods with applications

Reference 20

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Observation 4731f557-09dd-4967-935d-09d603117275 · outbound

This paper cites D-Wave Ocean Soft- ware Documentation, [Online].

Quantum Annealing based Feature Selection in Machine Learning D-Wave Ocean Soft- ware Documentation, [Online]

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:23.032602Z

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Observation 26677b0a-c287-4ff2-b6ea-26957f2c4156 · outbound

This paper cites Quantum computer based feature selection in machine learning.

Quantum Annealing based Feature Selection in Machine Learning Quantum computer based feature selection in machine learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:23.022142Z

Source-reported events for the cited work

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Observation 8e77973c-2b35-4fad-8c39-d3902f22d61d · outbound

This paper cites Breast Cancer Wisconsin (Diagnostic).

Quantum Annealing based Feature Selection in Machine Learning Breast Cancer Wisconsin (Diagnostic)

Reference 23

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This paper cites an unresolved cited work.

Quantum Annealing based Feature Selection in Machine Learning Unresolved cited work

Reference 24

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Observation 01a504e6-eb21-44df-950e-e624edfa44c1 · outbound

This paper cites Statlog (German Credit Data).

Quantum Annealing based Feature Selection in Machine Learning Statlog (German Credit Data)

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T06:06:22.465593Z

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Observation 4897ca3d-032d-4b53-b88b-cec957830449 · outbound

This paper cites Machine learning and deep learn- ing for phishing email classification using one-hot en- coding.

Quantum Annealing based Feature Selection in Machine Learning Machine learning and deep learn- ing for phishing email classification using one-hot en- coding

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:23.001392Z

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Observation 6bb7acc7-fee2-44eb-8e70-677f2ac4e705 · outbound

This paper cites E fficient approximate so- lutions to mutual information based global feature selec- tion.

Quantum Annealing based Feature Selection in Machine Learning E fficient approximate so- lutions to mutual information based global feature selec- tion

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:22.990802Z

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Observation 42913308-7762-4896-8ba0-6d00a6793ea7 · outbound

This paper cites A fast information- theoretic approximation of joint mutual information fea- ture selection.

Quantum Annealing based Feature Selection in Machine Learning A fast information- theoretic approximation of joint mutual information fea- ture selection

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:22.979860Z

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Observation 2e6ab548-9c2b-4b1c-a73c-547ed0cab56f · outbound

This paper cites E ffective global approaches for mutual information based feature selection.

Quantum Annealing based Feature Selection in Machine Learning E ffective global approaches for mutual information based feature selection

Reference 29

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Observation 20044e7a-1026-4791-bd77-5f4c2c42c260 · outbound

This paper cites Quantum annealing in the transverse Ising model.

Quantum Annealing based Feature Selection in Machine Learning Quantum annealing in the transverse Ising model

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:22.957964Z

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Observation 5905bf1b-ac5d-4293-ad6b-37d7222cae5a · outbound

This paper cites Op- timization by Simulated Annealing.

Quantum Annealing based Feature Selection in Machine Learning Op- timization by Simulated Annealing

Reference 31

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Observation bd1346d2-14ce-43f2-9e9f-f7789845e6f9 · outbound

This paper cites Theory of Quantum Annealing of an Ising Spin Glass.

Quantum Annealing based Feature Selection in Machine Learning Theory of Quantum Annealing of an Ising Spin Glass

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:22.944750Z

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Observation 13566d83-93a8-43c6-ae4c-52f75cdee791 · outbound

This paper cites Quantum annealing of the traveling-salesman prob- lem.

Quantum Annealing based Feature Selection in Machine Learning Quantum annealing of the traveling-salesman prob- lem

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:22.934774Z

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Observation a664d980-89e9-47ad-95e4-914d7166afc6 · outbound

This paper cites What is the Computational Value of Finite-Range Tunneling?.

Quantum Annealing based Feature Selection in Machine Learning What is the Computational Value of Finite-Range Tunneling?

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:22.924197Z

Source-reported events for the cited work

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Observation 41d7514f-9237-44ca-a99e-148d4e1ab45e · outbound

This paper cites Optimization by quantum annealing: Lessons from hard satisfiability problems.

Quantum Annealing based Feature Selection in Machine Learning Optimization by quantum annealing: Lessons from hard satisfiability problems

Reference 35

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Observation 7c3159ba-518c-4fac-9ca7-19031a4ba762 · outbound

This paper cites Deploying a quantum annealing processor to detect tree cover in aerial imagery of California.

Quantum Annealing based Feature Selection in Machine Learning Deploying a quantum annealing processor to detect tree cover in aerial imagery of California

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:22.903580Z

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Observation a1c14fa8-ecc3-496f-8549-5ae98c2ef4dc · outbound

This paper cites Tra ffic flow optimization using a quantum annealer.

Quantum Annealing based Feature Selection in Machine Learning Tra ffic flow optimization using a quantum annealer

Reference 37

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Observation 78cacbda-6906-49b7-8cb4-ea5de0e3f048 · outbound

This paper cites Nonnegative /binary matrix fac- torization with a D-Wave quantum annealer.

Quantum Annealing based Feature Selection in Machine Learning Nonnegative /binary matrix fac- torization with a D-Wave quantum annealer

Reference 38

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Observation 90ecf06a-7a13-4b31-8f54-13c1f415763a · outbound

This paper cites Adiabatic Quantum Compu- tation and Quantum Annealing.

Quantum Annealing based Feature Selection in Machine Learning Adiabatic Quantum Compu- tation and Quantum Annealing

Reference 39

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Observation d3ef717e-d33f-4a6e-b43c-817fe60e8bf9 · outbound

This paper cites Adiabatic quantum computation.

Quantum Annealing based Feature Selection in Machine Learning Adiabatic quantum computation

Reference 40

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Observation 0e75bf7a-6a22-4c74-ad89-ff2cf1f0ff6f · outbound

This paper cites Fluctuating interface with a pinning po- tential.

Quantum Annealing based Feature Selection in Machine Learning Fluctuating interface with a pinning po- tential

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:22.891018Z

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Observation e78b7d94-2024-444d-a85f-89d334deb429 · outbound

This paper cites On the theory of the Ising model of ferromagnetism.

Quantum Annealing based Feature Selection in Machine Learning On the theory of the Ising model of ferromagnetism

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:22.876285Z

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Observation e819d108-eb99-47a6-83b4-42fdd7de90c2 · outbound

This paper cites Multistart tabu search strategies for the unconstrained binary quadratic optimization problem.

Quantum Annealing based Feature Selection in Machine Learning Multistart tabu search strategies for the unconstrained binary quadratic optimization problem

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-12T06:06:22.533880Z

Source-reported events for the cited work

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Observation d3d7e3d3-3188-438c-908f-5b2a0b97dde9 · outbound

This paper cites LIBSVM: a li- brary for support vector machines.

Quantum Annealing based Feature Selection in Machine Learning LIBSVM: a li- brary for support vector machines

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:22.863183Z

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Observation 20768ff9-8174-4857-9346-5ea0d82763f5 · outbound

This paper cites New support vector algo- rithms.

Quantum Annealing based Feature Selection in Machine Learning New support vector algo- rithms

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:22.848440Z

Source-reported events for the cited work

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Observation 7d95bb1e-debd-44dd-bea1-8e2a91776eb5 · outbound

This paper cites Support vector re- gression model for the prediction of loadability mar- gin of a power system.

Quantum Annealing based Feature Selection in Machine Learning Support vector re- gression model for the prediction of loadability mar- gin of a power system

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:22.833274Z

Source-reported events for the cited work

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Observation 5bdcfdea-d8ef-4a77-8a06-deec60778acc · outbound

This paper cites Information theory.

Quantum Annealing based Feature Selection in Machine Learning Information theory

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T06:06:22.821187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

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