Paper Citation Record · LEDGER

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing

As of 11 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2607.03208.

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

pith.paper-citation-record.v1
2607.03208 v2

Coverage vector

measured 50 of 50 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

50 of 50 outbound references displayed

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External citation measurements

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Outbound references

Observation 0b13090e-34ff-4d4b-8af4-48dd72a2d892 · outbound

This paper cites URLhttps://doi.org/10.1038/s41586-022-04501-x.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing URLhttps://doi.org/10.1038/s41586-022-04501-x

Reference 1

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Observation dde3e47c-abeb-481f-a782-85f0979a278a · outbound

This paper cites The materials science behind sustainable metals and alloys.Chemical Reviews123, 2436–2608 (2023).

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing The materials science behind sustainable metals and alloys.Chemical Reviews123, 2436–2608 (2023)

Reference 2

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Observation 93709430-624c-4b7f-a4d4-6f48923ceb0b · outbound

This paper cites URLhttps://doi.org/10.1080/14686996.2023.2196242.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing URLhttps://doi.org/10.1080/14686996.2023.2196242

Reference 3

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Observation 592ec2d7-979d-49d6-8312-e36f4b442e18 · outbound

This paper cites URLhttps://doi.org/ 10.21203/rs.3.rs-10115028/v1.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing URLhttps://doi.org/ 10.21203/rs.3.rs-10115028/v1

Reference 4

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Observation aef72e84-11f5-4aad-8352-175ff806c715 · outbound

This paper cites M., Galarraga, H., Marquez, I.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing M., Galarraga, H., Marquez, I

Reference 5

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This paper cites D.et al.Beyond-classical computation in quantum simulation.Science388, 199–204 (2025).

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing D.et al.Beyond-classical computation in quantum simulation.Science388, 199–204 (2025)

Reference 6

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Observation 2506ee6f-5240-4f86-a4e2-f840bb7d008d · outbound

This paper cites URLhttps://doi.org/10.1038/s41534-025-01020-1.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing URLhttps://doi.org/10.1038/s41534-025-01020-1

Reference 7

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Observation c8da60a3-7571-40b4-87aa-aba09d0dcb18 · outbound

This paper cites & Lidar, D.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing & Lidar, D

Reference 8

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Observation 362d146a-2340-469f-a7e7-b8e546550a46 · outbound

This paper cites & Nishimori, H.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing & Nishimori, H

Reference 9

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Observation 2792fd66-1886-4875-9ede-bb0322b56840 · outbound

This paper cites G., Lechner, W., Nishimori, H.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing G., Lechner, W., Nishimori, H

Reference 10

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Observation af28fa15-b4a9-40f0-bfe8-7114f59b1300 · outbound

This paper cites & Schmitt, S.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing & Schmitt, S

Reference 11

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Observation e91c3ca5-cec8-42ba-8517-2e8a96de19c9 · outbound

This paper cites Factorization machines.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing Factorization machines

Reference 12

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Observation 9f1672df-1ad0-43f0-a7e1-4f0e128efd69 · outbound

This paper cites URLhttps://doi.org/10.1063/5.0275450.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing URLhttps://doi.org/10.1063/5.0275450

Reference 13

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Observation 0c094a70-74e6-4ce2-8ee6-4f8c6d7505ed · outbound

This paper cites an unresolved cited work.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing Unresolved cited work

Reference 14

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Observation 5f29c036-c21b-42ee-944d-9ec48ccd0823 · outbound

This paper cites URLhttps://pubs.acs.org/doi/10.1021/acsenergylett.2c01969.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing URLhttps://pubs.acs.org/doi/10.1021/acsenergylett.2c01969

Reference 15

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Observation c5509ec9-38df-489b-bf04-a5bfcbf1ca81 · outbound

This paper cites & Luo, T.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing & Luo, T

Reference 16

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Observation deafb633-4a04-482b-9eeb-228c37c9e7d0 · outbound

This paper cites & Tamura, R.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing & Tamura, R

Reference 17

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Observation c118bcac-938e-4616-84f0-c0f76904a82f · outbound

This paper cites & Miura, Y.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing & Miura, Y

Reference 18

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Observation 1b177cf5-7e30-4ea6-9360-8363abcea0b3 · outbound

This paper cites URLhttps://doi.org/10.7566/ JPSJ.94.044802.https://doi.org/10.7566/JPSJ.94.044802.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing URLhttps://doi.org/10.7566/ JPSJ.94.044802.https://doi.org/10.7566/JPSJ.94.044802

Reference 19

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Observation ec756be5-3883-4b8b-b777-e5c1dbdbbb9c · outbound

This paper cites & Luo, T.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing & Luo, T

Reference 20

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Observation 98cb62b3-696c-4efd-895e-11c7fd428031 · outbound

This paper cites & Lee, E.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing & Lee, E

Reference 21

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Observation 2a97bcd9-e198-497e-ab33-6a966f1f6bf1 · outbound

This paper cites & Melching, D.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing & Melching, D

Reference 22

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Observation a6f7ed50-6b3f-4749-a461-2cb146b098d1 · outbound

This paper cites D-Wave Systems Inc.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing D-Wave Systems Inc

Reference 23

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This paper cites D-wave advantage quantum computer (2024).

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing D-wave advantage quantum computer (2024)

Reference 24

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Observation 3165d4ae-6c71-4b8e-a317-78c758b9d176 · outbound

This paper cites & Sundman, B.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing & Sundman, B

Reference 25

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This paper cites [Accessed 24-04-2026].

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing [Accessed 24-04-2026]

Reference 26

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This paper cites URLhttp://dx.doi.org/10.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing URLhttp://dx.doi.org/10

Reference 27

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This paper cites URLhttps://advanced.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing URLhttps://advanced

Reference 28

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Observation 963bc8f9-6cf2-477f-9759-979aa8685e87 · outbound

This paper cites A., Eskin, D.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing A., Eskin, D

Reference 29

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Observation 9a1f947e-4ddc-4af0-886f-6bb392c096d8 · outbound

This paper cites A., Eskin, D.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing A., Eskin, D

Reference 30

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Observation 58e9edb7-6e56-4077-9bba-f309ce20727f · outbound

This paper cites A., Eskin, D.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing A., Eskin, D

Reference 31

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Observation c7ae98f5-2866-41ac-a3fe-75439054c99b · outbound

This paper cites & Skuli´ c, A.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing & Skuli´ c, A

Reference 32

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Observation ed19f661-1060-4f17-8718-67a0033e94e9 · outbound

This paper cites F.et al.Defining and detecting quantum speedup.Science345, 420–424 (2014).

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing F.et al.Defining and detecting quantum speedup.Science345, 420–424 (2014)

Reference 33

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Observation 203d7341-c989-4b84-9576-25c6b76cc1ec · outbound

This paper cites Performance benefits of increased qubit connectivity in quantum annealing 3-dimensional spin glasses.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing Performance benefits of increased qubit connectivity in quantum annealing 3-dimensional spin glasses

Reference 34

Resolution: unresolved; no resolver link, observed 2026-07-12T04:07:44.291228Z

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Observation db48f73e-8b3c-44f5-a505-92939ce6cba1 · outbound

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Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing Unresolved cited work

Reference 35

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Observation ecebab8c-2239-45a8-8dc6-d1020dd5299f · outbound

This paper cites M., Zilinskas, A.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing M., Zilinskas, A

Reference 36

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Observation 2fe0da8a-28b6-4851-a1ef-03559f1f6e14 · outbound

This paper cites an unresolved cited work.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing Unresolved cited work

Reference 37

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Observation 8b08ff19-eb7b-4a62-813a-70e4169b6b36 · outbound

This paper cites & Yamazaki, T.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing & Yamazaki, T

Reference 38

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Observation f91a4fe8-323c-435b-9fbd-32dd8e218c9b · outbound

This paper cites & Nishimori, H.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing & Nishimori, H

Reference 39

Resolution: verified exact; doi, observed 2026-07-12T04:08:31.623693Z

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Observation 50421a3c-db6d-4b0f-9d0b-dbdf48c42d83 · outbound

This paper cites & Jippo, H.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing & Jippo, H

Reference 40

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Observation c8dfe9f5-90b7-418d-ae41-c9b612fd5de2 · outbound

This paper cites & Aurich, J.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing & Aurich, J

Reference 41

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Observation fb9f1506-8ee6-4687-94be-d6b41f513acd · outbound

This paper cites [Accessed 24-04-2026].

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing [Accessed 24-04-2026]

Reference 42

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Observation 4c349e1c-f862-4d72-84a2-20a1a675ae53 · outbound

This paper cites [Accessed 24-04-2026].

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing [Accessed 24-04-2026]

Reference 43

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Observation cb91ea6a-8120-430d-a920-39dcf14ba0cb · outbound

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Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing & Borgenstam, A

Reference 44

Resolution: unresolved; no resolver link, observed 2026-07-12T04:07:44.291228Z

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Observation b46a5f38-521f-4c09-b9da-ee3e20a7da89 · outbound

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Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing & Brechet, Y

Reference 45

Resolution: unresolved; no resolver link, observed 2026-07-12T04:07:44.291228Z

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Observation fc08c781-cb44-4508-911e-275c1571a060 · outbound

This paper cites [Accessed 24-04- 2026].

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing [Accessed 24-04- 2026]

Reference 46

Resolution: unresolved; no resolver link, observed 2026-07-12T04:07:44.291228Z

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Observation f5cb09ed-dc2c-4296-8104-262ec6b677b4 · outbound

This paper cites & Wiedemann, G.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing & Wiedemann, G

Reference 47

Resolution: verified exact; doi, observed 2026-07-12T04:08:31.612580Z

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Observation 55917f28-9f90-4fff-b689-40fbbb701554 · outbound

This paper cites Frontiers in Quantum Science and Technology2(2023).

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing Frontiers in Quantum Science and Technology2(2023)

Reference 48

Resolution: verified exact; doi, observed 2026-07-12T04:08:31.652058Z

Source-reported events for the cited work

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Observation 737a7b68-380d-4640-b0a5-ff7a0c975ad0 · outbound

This paper cites fastfm: A library for factorization machines.Journal of Machine Learning Research17, 1–5 (2016).

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing fastfm: A library for factorization machines.Journal of Machine Learning Research17, 1–5 (2016)

Reference 49

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Observation 533b409e-8e71-4752-a4f8-a601751913d8 · outbound

This paper cites Optuna: A Next-generation Hyperparameter Optimization Framework.

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing Optuna: A Next-generation Hyperparameter Optimization Framework

Reference 50

Resolution: unresolved; no resolver link, observed 2026-07-12T04:07:44.291228Z

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Pith citing papers

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