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Paper Citation Record · LEDGER

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials

As of 9 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 0 inbound Pith citation observations for arXiv:2602.16908.

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

pith.paper-citation-record.v1
2602.16908 v2

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T22:26:58.396663Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

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

61 of 61 outbound references displayed

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  • verified fuzzy0
  • unresolved59
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External citation measurements

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Outbound references

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This paper cites an unresolved cited work.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work

Reference 1

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This paper cites an unresolved cited work.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work

Reference 2

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Observation 8979463c-8bb3-486e-b4a8-f6eb9afba817 · outbound

This paper cites Mueller, A.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Mueller, A

Reference 3

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This paper cites Argaman and G.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Argaman and G

Reference 4

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work

Reference 5

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work

Reference 6

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work

Reference 7

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work

Reference 8

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work

Reference 9

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This paper cites Drautz, Atomic cluster expansion for accurate and transferable interatomic potentials, Physical Review B 99, 014104 (2019).

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Drautz, Atomic cluster expansion for accurate and transferable interatomic potentials, Physical Review B 99, 014104 (2019)

Reference 10

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This paper cites Lysogorskiy, C.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Lysogorskiy, C

Reference 11

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Observation 73c55887-85cd-42e4-b219-a955f9618f55 · outbound

This paper cites Batzner, A.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Batzner, A

Reference 12

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This paper cites Musaelian, S.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Musaelian, S

Reference 13

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This paper cites Sagingalieva, M.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Sagingalieva, M

Reference 14

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Sagingalieva, S

Reference 15

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Lusnig, A

Reference 16

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Sedykh, M

Reference 17

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Anoshin, A

Reference 18

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Observation 211492b7-8335-463a-8be6-6a068eb62ab7 · outbound

This paper cites Sagingalieva, L.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Sagingalieva, L

Reference 19

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Tsurkan, A

Reference 20

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Kurkin, J

Reference 21

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work

Reference 22

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials de Winter, B

Reference 23

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Ruddigkeit, R

Reference 24

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Ramakrishnan, P

Reference 25

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work

Reference 26

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Grisafi, D

Reference 27

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Zhang, J

Reference 28

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Kozinsky, A

Reference 30

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Learning Interatomic Potentials at Multiple Scales

Reference 31

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials What is an equivariant neural network?

Reference 32

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Observation 0adbe9c4-4110-466f-8279-0942035504a0 · outbound

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Cohen and M

Reference 33

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Navon, A

Reference 35

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Zur and T

Reference 37

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work

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Observation 0ad5b0a8-62e3-46f4-ac60-3aef38c08d15 · outbound

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Kresse and J

Reference 41

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Observation 1f9ff15f-ae4f-4f8b-a5a0-b93346444b90 · outbound

This paper cites Kresse and J.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Kresse and J

Reference 42

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Observation 2fbdca97-df83-46ce-b3a2-0423f4309a9e · outbound

This paper cites Kresse and D.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Kresse and D

Reference 43

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Observation 409bdd84-6aed-48be-9065-927fc460541c · outbound

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Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work

Reference 44

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Observation b5f3234e-1df1-46af-9eb5-6afe32964efd · outbound

This paper cites an unresolved cited work.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work

Reference 45

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Observation 7806c95c-7699-449a-bfe3-69bfeea5a0c9 · outbound

This paper cites Chmiela, H.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Chmiela, H

Reference 46

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Observation ffaa0304-4416-47f8-b38f-ab0cb5c3fb1a · outbound

This paper cites Ramakrishnan, P.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Ramakrishnan, P

Reference 47

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Observation aadde761-b2bf-45d2-9c78-69313ed3e82e · outbound

This paper cites Batzner, A.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Batzner, A

Reference 48

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Observation 06edfd1a-06f6-49d8-becd-a39e83160fb2 · outbound

This paper cites Musaelian, S.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Musaelian, S

Reference 49

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Observation 5413a78e-519a-4ebb-bd61-b53144377952 · outbound

This paper cites an unresolved cited work.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work

Reference 50

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Observation fdc9f831-0bd4-4c3c-9f8e-7ea7ba171165 · outbound

This paper cites de Zomer and M.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials de Zomer and M

Reference 51

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Observation 8128f241-1af3-4dde-9095-7d5ec99efa70 · outbound

This paper cites Coecke and R.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Coecke and R

Reference 52

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Observation 931deb1f-dc3d-4af1-be05-a022a958bb78 · outbound

This paper cites ZX-calculus for the working quantum computer scientist.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials ZX-calculus for the working quantum computer scientist

Reference 53

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source=pdf_text observed=2026-08-02T22:26:57.651096Z digest=sha256:1a8a855b7b947186db8333bda732aa185a59bdfdad9b59dbbfff7a53b6eb71d9

Observation 22b0f909-6310-423c-913d-6dd41b035bce · outbound

This paper cites The power of quantum neural networks.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials The power of quantum neural networks

Reference 54

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Observation a9c48186-8224-4d05-88b2-df30bd4637dd · outbound

This paper cites Amari, Natural gradient works efficiently in learning, Neural Computation10, 251 (1998).

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Amari, Natural gradient works efficiently in learning, Neural Computation10, 251 (1998)

Reference 55

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Observation 48855d52-e9ab-4866-b3bb-d314dd087d17 · outbound

This paper cites an unresolved cited work.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work

Reference 56

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Observation af8001cf-a2a3-4c68-81be-25c2348c748d · outbound

This paper cites Larocca, N.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Larocca, N

Reference 57

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Observation 410b4e28-d8ba-4aaa-a379-cae91719ed3b · outbound

This paper cites Exponentially Weighted Moving Models.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Exponentially Weighted Moving Models

Reference 58

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Observation 36923f4e-1cbf-45bf-9961-c82e2106f1aa · outbound

This paper cites After this ZX graph can be simplified [52] using language’s set of rules, thus taking out redundant elements [53].

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials After this ZX graph can be simplified [52] using language’s set of rules, thus taking out redundant elements [53]

Reference 59

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Observation 07e85973-7375-4eaf-b65a-44ec2e3515d6 · outbound

This paper cites Given a subsetSof labeled samples from this distribution, we aim to optimize our model to accurately approximatef(ˆx).

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Given a subsetSof labeled samples from this distribution, we aim to optimize our model to accurately approximatef(ˆx)

Reference 60

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Observation bcca4044-ec03-41ed-b906-298623ed995c · outbound

This paper cites For a feature vector of lengthN, this Fourier series is expressed as a function of the fea- ture vectorxand the trainable parametersθ: fθ(x) = X ω1∈Ω1.

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials For a feature vector of lengthN, this Fourier series is expressed as a function of the fea- ture vectorxand the trainable parametersθ: fθ(x) = X ω1∈Ω1

Reference 61

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