Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-02T22:26:58.396663Z
Paper Citation Record · LEDGER
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.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-02T22:26:58.396663Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
61 of 61 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation b046aa21-ac4f-4f9c-9f3d-9e6a83aa4842 · outbound
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work
Reference 1
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Observation b06c84f5-d85d-4ab6-9c63-1c77d63ce0c5 · outbound
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
Reference 3
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Observation 3c2af249-33fd-47da-bdeb-89e8bd507305 · outbound
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Argaman and G
Reference 4
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Observation ec46533d-db8b-463f-9445-66025134c104 · outbound
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work
Reference 5
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Observation 6d1e75df-3a03-419c-bdde-c4105d65d2bd · outbound
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work
Reference 6
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Observation c9373ced-4b59-45b8-80db-77cd77496611 · outbound
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work
Reference 7
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Observation adf33b14-5908-41b5-bd18-7c4af6bf7586 · outbound
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work
Reference 8
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Observation 12820ec2-6f00-4995-9ac3-467e2503f6ab · outbound
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work
Reference 9
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Unavailable: canonical work link unavailable.
Observation 9f6fa4b1-fc77-470f-adcd-5b61354a4d99 · outbound
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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Unavailable: canonical work link unavailable.
Observation 9cb7fb01-12aa-42cf-850e-d955a3c8870f · outbound
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
Reference 12
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Observation 7db56057-73c9-4f6c-b1fc-8006509e150b · outbound
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Musaelian, S
Reference 13
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Observation 71bec847-87ca-4364-aba7-863049c397d5 · outbound
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Sagingalieva, M
Reference 14
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Observation 8e748a30-9294-4bde-865e-862766e32e86 · outbound
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Sagingalieva, S
Reference 15
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Observation e480c141-4b17-4c4b-b3d8-d897d8d30813 · outbound
Reference 16
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Observation c1ec6819-2eac-44af-bf7e-64b15cde212b · outbound
Reference 17
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Observation 1c55e727-6118-4006-b556-dc109e30d8a0 · outbound
Reference 18
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Observation 211492b7-8335-463a-8be6-6a068eb62ab7 · outbound
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Sagingalieva, L
Reference 19
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Observation c5f580a6-a04c-4163-b837-954ae40a5f0b · outbound
Reference 20
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Observation d21d7f1b-179a-47ea-a4ae-1b7e3f6657a1 · outbound
Reference 21
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Observation 8c725f5a-61cf-4098-a5cf-10aaec8ded45 · outbound
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work
Reference 22
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Observation 944d60e2-baee-4725-9f84-d506ad6bafbb · outbound
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials de Winter, B
Reference 23
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Observation dd812d20-0ce6-41a1-8fed-1a64be36e123 · outbound
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Ruddigkeit, R
Reference 24
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Unavailable: canonical work link unavailable.
Observation 727b79ed-74ae-4744-8a8e-4d208a678361 · outbound
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Ramakrishnan, P
Reference 25
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Observation 88802bbf-3844-4669-8131-4f7f79d3ee85 · outbound
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work
Reference 26
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation b6511355-8a8d-41b9-8bef-90397e340928 · outbound
Reference 27
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Observation affe53bc-ff29-4ba9-bbf0-b2beaf761adc · outbound
Reference 28
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Observation 0a76c4e7-0479-473f-89c9-1437eb19b274 · outbound
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work
Reference 29
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Observation c1a71e22-86d4-4a40-9b07-0f6b2b788ec5 · outbound
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Kozinsky, A
Reference 30
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Observation 8cd58ee7-f080-4fbb-af84-34dd1c5da08d · outbound
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Learning Interatomic Potentials at Multiple Scales
Reference 31
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Observation 22207bfd-b825-4d67-877e-c4a0482bad81 · outbound
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
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Cohen and M
Reference 33
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Observation 2d7512be-9a34-4ea2-9a15-88ca8eb204c4 · outbound
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work
Reference 34
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Observation c2e6922a-2c31-4384-969c-52ca40bd346a · outbound
Reference 35
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Observation 35ace02c-445e-4593-a553-b286e066cd7e · outbound
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work
Reference 36
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Observation 15bf154e-c30a-4216-8daf-ebb6d2fee64f · outbound
Reference 37
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Observation 80ff2cc5-5ed3-4a6c-ab78-48020f7a3366 · outbound
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work
Reference 38
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Observation c274386e-8ed6-42a3-9fef-85399b37ce00 · outbound
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work
Reference 39
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 7bc4ae33-a43f-49f1-ab27-320de72df55f · outbound
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials Unresolved cited work
Reference 40
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Observation 0ad5b0a8-62e3-46f4-ac60-3aef38c08d15 · outbound
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
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
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
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
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
Reference 46
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Observation ffaa0304-4416-47f8-b38f-ab0cb5c3fb1a · outbound
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
Reference 48
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Observation 06edfd1a-06f6-49d8-becd-a39e83160fb2 · outbound
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
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
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
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
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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Observation 22b0f909-6310-423c-913d-6dd41b035bce · outbound
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
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
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
Reference 57
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Observation 410b4e28-d8ba-4aaa-a379-cae91719ed3b · outbound
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
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
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
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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No inbound Pith citation observations are available.