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Paper Citation Record · LEDGER

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding

As of 15 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2606.00932.

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

pith.paper-citation-record.v1
2606.00932 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T18:16:29.613241Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

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

34 of 34 outbound references displayed

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External citation measurements

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Outbound references

Observation b140d66f-4052-4b0f-b625-d3fea8b4a603 · outbound

This paper cites Their hyperparameters are selected once on a pi- lot validation split and then fixed across the learning-curve runs.

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Their hyperparameters are selected once on a pi- lot validation split and then fixed across the learning-curve runs

Reference 1

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Observation 9da2931f-abd2-4335-b638-991cd94fc50b · outbound

This paper cites Biamonte, P.

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Biamonte, P

Reference 2

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source=pdf_text observed=2026-06-28T18:16:29.613241Z digest=sha256:d311db6f678250a32eb170e7433e68e78aaa94b1214a7204d127c60794def72b

Observation 08e628f4-545a-49f6-8152-387d1e9a14ff · outbound

This paper cites Ciliberto, M.

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Ciliberto, M

Reference 3

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source=pdf_text observed=2026-06-28T18:16:29.613241Z digest=sha256:44d6ef85b0ea915690ca24793aed7586155d244195919f26afdbaaabf0f7e127

Observation 8b4b8466-9749-4545-9199-4da4e8a023f0 · outbound

This paper cites Havl´ıˇcek, A.

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Havl´ıˇcek, A

Reference 4

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source=pdf_text observed=2026-06-28T18:16:29.613241Z digest=sha256:2f9321e5a9e65f70a633b85435dfdb769c81e2f70b20050d3e2c4b06171a68bf

Observation ce5b1a92-e4ff-4060-a950-d2d596a59906 · outbound

This paper cites Schuld and N.

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Schuld and N

Reference 5

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source=pdf_text observed=2026-06-28T18:16:29.613241Z digest=sha256:d9452e71068f97e5b1abcf0c376a908c6016aac66c96d830d8918575f783e79c

Observation a786b18a-7a82-458b-a2e3-b61e63522870 · outbound

This paper cites Cerezo, G.

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Cerezo, G

Reference 6

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source=pdf_text observed=2026-06-28T18:16:29.613241Z digest=sha256:0f3d385bf3ec7cf57d87b78820088e0ffdb05b2f8ed9fdb01b19d3fca73890f8

Observation c24f3fbc-93d8-48fa-b928-dfec0459cca7 · outbound

This paper cites an unresolved cited work.

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Unresolved cited work

Reference 7

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source=pdf_text observed=2026-06-28T18:16:29.613241Z digest=sha256:6650e31b77194ff7233874102b142580fb60f885e3b3e60ce6471431d0269529

Observation eef4edb5-8bcc-4019-8cb7-5196a0138055 · outbound

This paper cites Preskill, Quantum2, 79 (2018).

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Preskill, Quantum2, 79 (2018)

Reference 8

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source=pdf_text observed=2026-06-28T18:16:29.613241Z digest=sha256:8148216f68822807bec9707b49adc02a39c08a99470a9126c481161bac57466e

Observation 5e0ab492-a4cb-4726-a05d-363ad8adef95 · outbound

This paper cites Schuld and N.

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Schuld and N

Reference 9

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source=pdf_text observed=2026-06-28T18:16:29.613241Z digest=sha256:9ad1e8a3af90679b8874ca1e061c383c2c15b6f6cee637e6109729f5139e9ab4

Observation 80ae169b-5250-4957-97f0-0917ee241f90 · outbound

This paper cites Aaronson, Nature Physics11, 291 (2015).

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Aaronson, Nature Physics11, 291 (2015)

Reference 10

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source=pdf_text observed=2026-06-28T18:16:29.613241Z digest=sha256:acabd70b4ac8e506c166caa9a75164cb93f5b6219ec01e3d0b7352e398e557c3

Observation 08af2d85-a2fe-4709-9750-3483675766eb · outbound

This paper cites Paler, O.

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Paler, O

Reference 11

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source=pdf_text observed=2026-06-28T18:16:29.613241Z digest=sha256:4f7ce1ddfd0c20a5af9cd1f40eb6e0907d0fc675b0f843fad2a44de8fa2ddb98

Observation c7d33e70-8c02-492c-9a4c-f8d1f8ebfad7 · outbound

This paper cites Small quantum computers and large classical data sets.

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Small quantum computers and large classical data sets

Reference 12

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Observation 69b6c029-ad82-4208-93f7-d6c9d7102e5d · outbound

This paper cites Tang, Physical Review Letters127, 060503 (2021).

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Tang, Physical Review Letters127, 060503 (2021)

Reference 13

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source=pdf_text observed=2026-06-28T18:16:29.613241Z digest=sha256:9c91a34150c903043b8d985d56194c7e2a2310cb23b01d0978c6fdd3b45e82dc

Observation a24bb19f-4791-4d3c-91b7-5e9ed2d0a112 · outbound

This paper cites Jerbi, L.

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Jerbi, L

Reference 14

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source=pdf_text observed=2026-06-28T18:16:29.613241Z digest=sha256:baef150e1bef237d76b6077593d9df48a19e1571e78676a6a777638e25ac85dc

Observation e3bac286-b67d-4262-9edb-042de890fba5 · outbound

This paper cites Huang, M.

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Huang, M

Reference 15

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source=pdf_text observed=2026-06-28T18:16:29.613241Z digest=sha256:5616194a4bb2b91477e368e3d85310715895761c5fb2fb4d58d7d106f59c4bd6

Observation 698515fc-38a5-461e-b90d-7b93644e6587 · outbound

This paper cites Thanasilp, S.

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Thanasilp, S

Reference 16

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Observation 378d40e4-4866-40a4-86c6-23f4a82ed4c0 · outbound

This paper cites Agliardi, G.

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Agliardi, G

Reference 17

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Observation fd72d716-16c7-424c-b1c3-b66d22ac38f8 · outbound

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Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Unresolved cited work

Reference 18

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Observation 0358858b-e704-4cad-b2fb-6ab5e3e0e47e · outbound

This paper cites an unresolved cited work.

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Unresolved cited work

Reference 19

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Observation 6ea164c6-0115-463f-80f1-64937fb75c34 · outbound

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Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Unresolved cited work

Reference 20

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Observation da2e0eaa-75e7-4d36-9e1d-09eaa22f722e · outbound

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Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Unresolved cited work

Reference 21

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Observation 8269252e-c307-44c0-a226-becf9316cd96 · outbound

This paper cites Abbas, D.

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Abbas, D

Reference 22

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Observation 09178401-91c1-415d-a566-f63782413ed1 · outbound

This paper cites Huang, R.

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Huang, R

Reference 23

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Observation 77ac8537-3776-4ec7-a6ad-7af920d6a035 · outbound

This paper cites Caponnetto and E.

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Caponnetto and E

Reference 24

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Observation a428e520-de77-4cda-84a3-f03f0f6bb316 · outbound

This paper cites Huang, M.

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Huang, M

Reference 25

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Observation 399c2735-656a-4aee-b5a7-2335eb13f5ea · outbound

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Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Unresolved cited work

Reference 26

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Observation e0478c0d-dc64-41ab-9412-45991c8c420a · outbound

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Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Unresolved cited work

Reference 27

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Observation 0bf5adcf-54c1-4708-a177-66c822dbc886 · outbound

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Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Unresolved cited work

Reference 28

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Observation 4fb830b1-c0e6-4df7-9fb7-3249fd7590e0 · outbound

This paper cites Angluin and P.

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Angluin and P

Reference 29

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Observation 3b85adc6-65f0-49b7-9add-73f3c9dc8245 · outbound

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Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Unresolved cited work

Reference 30

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Observation 8105f695-da1a-4bd0-a015-9db00dc1df44 · outbound

This paper cites Cerezo, A.

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Cerezo, A

Reference 31

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Observation fc4c473d-fb1b-4335-912e-0f3438895b9e · outbound

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Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Unresolved cited work

Reference 32

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Observation bcd11750-772b-4dd9-8b5c-938e3b7c2e13 · outbound

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Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Unresolved cited work

Reference 33

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Observation 5ca70d4a-5c30-4fda-b875-cea0418c1c85 · outbound

This paper cites Cortes and V.

Learning with Active Quantum Subspaces: Scalable Hybrid Advantage without Full Quantum Data-Encoding Cortes and V

Reference 34

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Pith citing papers

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