Paper Citation Record · LEDGER

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency

As of 10 August 2026, this Paper Citation Record lists 74 of 74 outbound references and 1 inbound Pith citation observation for arXiv:2607.24014.

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

pith.paper-citation-record.v1
2607.24014 v1

Coverage vector

measured 74 of 74 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-31T23:26:47.399712Z

measured 75 of 75 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:30:47.665090Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T00:30:50.821017Z

Reference resolution

74 of 74 outbound references displayed

verified exact0
verified fuzzy0
unresolved74
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External citation measurements

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Outbound references

Observation 1fe05b8f-57d8-40c9-9122-7722ea39d161 · outbound

This paper cites MMD), which inherits polynomial gradient variance from the local-observable analysis applied to Mercer features of the kernel.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency MMD), which inherits polynomial gradient variance from the local-observable analysis applied to Mercer features of the kernel

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-31T23:26:40.014611Z

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Observation dead273c-0083-44f0-aaf5-a9b5e1771c03 · outbound

This paper cites fermion sampling with less magic.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency fermion sampling with less magic

Reference 2

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Observation f6c6f072-24c4-4729-9153-8b42e4d0e273 · outbound

This paper cites Reardon-Smith, M.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Reardon-Smith, M

Reference 3

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Observation 15b2028e-50c1-484c-b155-80734397cc58 · outbound

This paper cites Classical simulation of free-fermionic dynamics and quantum chemistry with magic input.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Classical simulation of free-fermionic dynamics and quantum chemistry with magic input

Reference 4

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Observation 3183705b-07b6-473b-bb93-c50f75877f2f · outbound

This paper cites Oszmaniec, N.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Oszmaniec, N

Reference 5

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Observation 8c35434a-7a38-425d-ad75-787af304b06c · outbound

This paper cites an unresolved cited work.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Unresolved cited work

Reference 6

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Observation 0e4d62bd-e14d-4e91-aff5-1574d9b276d8 · outbound

This paper cites Kerenidis and A.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Kerenidis and A

Reference 7

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Observation 3ff97260-48be-4d7b-bad0-5ba352a2fae8 · outbound

This paper cites Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics

Reference 8

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Observation baae9742-9d68-46b2-9f7d-ecfddd7e56f5 · outbound

This paper cites Quantum algorithms for supervised and unsupervised machine learning.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Quantum algorithms for supervised and unsupervised machine learning

Reference 9

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Observation 5e77afb3-8fe1-4822-bac0-b416358f0c89 · outbound

This paper cites Lloyd, M.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Lloyd, M

Reference 10

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Observation 6c7feef9-8144-4593-ac60-fd9a48fccd63 · outbound

This paper cites Kerenidis, J.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Kerenidis, J

Reference 11

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Observation 8ea36a50-915c-47bc-91f9-6a7b1a5acf33 · outbound

This paper cites A quantum-inspired classical algorithm for recommendation systems.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency A quantum-inspired classical algorithm for recommendation systems

Reference 12

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Observation 9760756c-9185-4b6e-8535-419acc82740b · outbound

This paper cites Quantum principal component analysis only achieves an exponential speedup because of its state preparation assumptions.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Quantum principal component analysis only achieves an exponential speedup because of its state preparation assumptions

Reference 13

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Observation 9440c4fe-5703-4ba2-b807-10dabab49c44 · outbound

This paper cites Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning

Reference 14

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Observation 665af7f0-b623-45d5-a0bd-2e8440749743 · outbound

This paper cites Exponential quantum advantage in processing massive classical data.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Exponential quantum advantage in processing massive classical data

Reference 15

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Observation ccc488cd-9e5e-49f8-b0c8-9e48ed568800 · outbound

This paper cites Mitarai, M.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Mitarai, M

Reference 16

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Observation 6248da6b-b2ba-496d-9d39-857c3a127569 · outbound

This paper cites Schuld and N.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Schuld and N

Reference 17

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Observation 586d4fe0-93dc-4542-b484-83a1ae71afaf · outbound

This paper cites Benedetti, E.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Benedetti, E

Reference 18

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Observation dc641d18-fc23-4e35-9ef6-f4d9dd600c49 · outbound

This paper cites P´ erez-Salinas, A.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency P´ erez-Salinas, A

Reference 19

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Observation 00572f7d-c1d1-4cb1-bf7f-fe1416976bbe · outbound

This paper cites Biamonte, P.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Biamonte, P

Reference 20

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Observation 379a2bcc-9a67-4778-bc73-5a99a800311f · outbound

This paper cites Cerezo, A.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Cerezo, A

Reference 21

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Observation 4997d681-9dbc-4457-91bc-e27e7d46d1cd · outbound

This paper cites Classification with Quantum Neural Networks on Near Term Processors.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Classification with Quantum Neural Networks on Near Term Processors

Reference 22

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Observation fadb1be1-51c8-4818-8fed-de80ae049277 · outbound

This paper cites Havl ´ ıˇ cek, A.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Havl ´ ıˇ cek, A

Reference 23

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Observation 1e398f00-3601-4373-aefd-9b49590fbeb0 · outbound

This paper cites Abbas, D.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Abbas, D

Reference 24

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Observation e23d175b-88e0-44e5-a1dd-190f4aa125dd · outbound

This paper cites Landman, N.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Landman, N

Reference 25

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Observation 9753293f-eeb9-4d85-a319-105af157b5af · outbound

This paper cites Dunjko, J.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Dunjko, J

Reference 26

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Observation 2ab95fae-c53b-4d20-9b7f-64d3e1b17926 · outbound

This paper cites Jerbi, C.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Jerbi, C

Reference 27

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Observation 4d739696-5b2c-439d-98d4-e4e9cdcf996c · outbound

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Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Unresolved cited work

Reference 28

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Observation 28c65462-d869-4744-8763-932ae967a8b1 · outbound

This paper cites Schuld, I.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Schuld, I

Reference 29

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Observation 3775441c-3cce-47fa-b0d9-5e532878455f · outbound

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Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Unresolved cited work

Reference 30

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Observation 3e60abd0-bd65-4b60-9d3f-1e3feae3129a · outbound

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Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Unresolved cited work

Reference 31

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Observation 0f28f806-8145-478d-935b-0f25e9bb775a · outbound

This paper cites Pesah, M.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Pesah, M

Reference 32

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Observation 6801f2d7-c335-4af6-b9ad-ac1f97f679a3 · outbound

This paper cites Grant, M.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Grant, M

Reference 33

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Observation 74f0dff6-d839-4fdc-a9be-a14dc70428b2 · outbound

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Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Unresolved cited work

Reference 34

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Observation 3be22b1e-0d88-4d3b-8b54-4ec0318b5e7c · outbound

This paper cites Larocca, F.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Larocca, F

Reference 35

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Observation d496d3aa-eb5c-46b3-ae9b-443eaab51034 · outbound

This paper cites Fontana, D.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Fontana, D

Reference 36

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Observation 16ea6b32-6b7e-4ef9-b0b7-f8d7458ec1e7 · outbound

This paper cites Larocca, P.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Larocca, P

Reference 37

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Observation f55d43df-53eb-474b-9b67-b6d8d5f416c7 · outbound

This paper cites Cerezo, M.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Cerezo, M

Reference 38

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Observation a185195c-291f-4f84-ba30-5345472783a2 · outbound

This paper cites Bermejo, P.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Bermejo, P

Reference 39

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Observation 08ceb803-7f73-4cb7-9149-323f3412e7df · outbound

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Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Unresolved cited work

Reference 40

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Observation 1cefa57f-0743-4dee-a382-523fbee14712 · outbound

This paper cites Classical and Quantum Algorithms for Orthogonal Neural Networks.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Classical and Quantum Algorithms for Orthogonal Neural Networks

Reference 41

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Observation 0a96398d-1829-407f-b32f-f98e92b9a1cd · outbound

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Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Unresolved cited work

Reference 42

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Observation ad794f89-9aca-446f-8d20-5e46f9f75491 · outbound

This paper cites Thakkar, S.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Thakkar, S

Reference 43

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Observation 8367ae36-1e5c-442c-b0a1-81771d6fd260 · outbound

This paper cites Kazdaghli, I.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Kazdaghli, I

Reference 44

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Observation 731c43af-ed19-4032-8c72-4f2c9a828568 · outbound

This paper cites Scalable On-Hardware Training of Quantum Neural Networks and Application to Clinical Data Imputation.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Scalable On-Hardware Training of Quantum Neural Networks and Application to Clinical Data Imputation

Reference 45

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Observation 97fcf79e-e6f0-4b5a-a54e-1a5d153797ce · outbound

This paper cites Jozsa and A.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Jozsa and A

Reference 46

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Observation d26177b7-bc13-418d-93f7-d521cdfaeb7d · outbound

This paper cites Monbroussou, E.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Monbroussou, E

Reference 47

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Observation ff89a669-fce6-4633-8011-45c1fe1d6c6b · outbound

This paper cites Schuld, V.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Schuld, V

Reference 48

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Observation c5d6fec3-b764-4d74-b24f-0963b5cac871 · outbound

This paper cites Gacon, C.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Gacon, C

Reference 49

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Observation b08b9dd0-565d-480f-9fdf-1ac908109469 · outbound

This paper cites Wierichs, J.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Wierichs, J

Reference 50

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Observation 96723b18-d48e-43e7-a843-991ddab2afb9 · outbound

This paper cites Adaptive directional gradients for parameterised quantum circuits.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Adaptive directional gradients for parameterised quantum circuits

Reference 51

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Observation 11094f2b-5e79-46c3-9068-156c3d202458 · outbound

This paper cites Coyle, S.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Coyle, S

Reference 52

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Observation be3145a2-c787-4260-ac74-26bba99fe303 · outbound

This paper cites B¨ artschi and S.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency B¨ artschi and S

Reference 53

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Observation b197c9e0-c55d-4bcf-9e05-a19e3fe18d18 · outbound

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Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Unresolved cited work

Reference 54

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Observation 38723bf9-6619-48b4-9f51-585e82392ec9 · outbound

This paper cites Schuld, R.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Schuld, R

Reference 55

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Observation e75d1a68-2b7d-4c34-933c-2e318491b4ed · outbound

This paper cites Hyperpfaffians and Geometric Complexity Theory.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Hyperpfaffians and Geometric Complexity Theory

Reference 56

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Observation d4b44570-0dc2-4b6c-b738-b935a1c8b77c · outbound

This paper cites Hebenstreit, R.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Hebenstreit, R

Reference 57

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Observation e3ba2def-e303-4f25-91b5-25c6f6971c9b · outbound

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Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Unresolved cited work

Reference 58

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Observation e0af3fd9-9095-4d9d-9111-466188479a13 · outbound

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Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Unresolved cited work

Reference 59

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Observation 6089172e-cd53-402e-a96d-b5674d1c4ff6 · outbound

This paper cites Knill,Fermionic linear optics and matchgates, Tech.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Knill,Fermionic linear optics and matchgates, Tech

Reference 60

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Observation e844da9e-3d1b-4e37-ba5c-b9bef3b7e01d · outbound

This paper cites Cerezo, A.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Cerezo, A

Reference 61

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Observation e93f329d-30d4-4f8b-9a1c-879806da6f82 · outbound

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Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Unresolved cited work

Reference 62

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Observation bd6ac909-d026-4b9c-a5f2-f56f318492a7 · outbound

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Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Unresolved cited work

Reference 63

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Observation 1be6cd30-d225-4e2a-bf0f-1101f299ca78 · outbound

This paper cites Coyle, D.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Coyle, D

Reference 64

Resolution: unresolved; no resolver link, observed 2026-07-31T23:26:46.694241Z

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Observation e8fb94d0-e237-4f48-970e-914370984058 · outbound

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Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Unresolved cited work

Reference 65

Resolution: unresolved; no resolver link, observed 2026-07-31T23:26:46.208377Z

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Unavailable: canonical work link unavailable.

Observation be5a2413-68d7-4d77-9ae5-db385d900819 · outbound

This paper cites Nesterov, Efficiency of coordinate descent methods on huge-scale optimization problems, SIAM Journal on Optimization22, 341 (2012).

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Nesterov, Efficiency of coordinate descent methods on huge-scale optimization problems, SIAM Journal on Optimization22, 341 (2012)

Reference 66

Resolution: unresolved; no resolver link, observed 2026-07-31T23:26:46.295902Z

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Unavailable: canonical work link unavailable.

Observation b7c9e6df-68d6-49c0-9e5d-850b5dfad7f8 · outbound

This paper cites an unresolved cited work.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Unresolved cited work

Reference 67

Resolution: unresolved; no resolver link, observed 2026-07-31T23:26:47.016298Z

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Unavailable: canonical work link unavailable.

Observation 215a844b-0dac-4d6f-9f15-53e5edba49d6 · outbound

This paper cites Lloyd and C.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Lloyd and C

Reference 68

Resolution: unresolved; no resolver link, observed 2026-07-31T23:26:46.837506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19f6942e-95a7-4b6f-9c2d-ee230ee7db0a · outbound

This paper cites Schuld and F.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Schuld and F

Reference 69

Resolution: unresolved; no resolver link, observed 2026-07-31T23:26:46.923327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e141d5d0-46e3-4325-ac90-91acd3d3fefa · outbound

This paper cites an unresolved cited work.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Unresolved cited work

Reference 71

Resolution: unresolved; no resolver link, observed 2026-07-31T23:26:47.119742Z

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Observation 12f9ed5a-5553-4a71-af53-ac52e9ca9754 · outbound

This paper cites Proof.Tracing over the second factor setsd=band sums overb: [Tr2 Φ2[Y]] a,c = X b,e,f,g,h E[WaeWbf W ∗ cgW ∗ bh]Y (e,f),(g,h).

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Proof.Tracing over the second factor setsd=band sums overb: [Tr2 Φ2[Y]] a,c = X b,e,f,g,h E[WaeWbf W ∗ cgW ∗ bh]Y (e,f),(g,h)

Reference 72

Resolution: unresolved; no resolver link, observed 2026-07-31T23:26:47.230241Z

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Unavailable: canonical work link unavailable.

Observation ce6103e0-f38b-4fc1-9bf8-818f5f9c1313 · outbound

This paper cites an unresolved cited work.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Unresolved cited work

Reference 73

Resolution: unresolved; no resolver link, observed 2026-07-31T23:26:47.333653Z

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Observation ed93302e-06e1-4ccb-84f9-374a1b4bcd82 · outbound

This paper cites Lemma 41(Closed basis).Φ Wn 2 [Pij]∈span{P kl :k < l}for alli < j.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Lemma 41(Closed basis).Φ Wn 2 [Pij]∈span{P kl :k < l}for alli < j

Reference 74

Resolution: unresolved; no resolver link, observed 2026-07-31T23:26:47.399712Z

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Observation 397c472d-9849-4c69-a4ed-a329c32bf26f · outbound

This paper cites Fermionic Linear Optics and Matchgates.

Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency Fermionic Linear Optics and Matchgates

Reference 2001

Resolution: unresolved; no resolver link, observed 2026-07-31T23:26:45.856209Z

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Unavailable: canonical work link unavailable.

Pith citing papers

Observation 0b849e63-dddb-417e-82e7-1e19cff2fd37 · inbound

Hybrid Quantum Neural Networks: Theory, Implementations, and Applications cites this paper.

Hybrid Quantum Neural Networks: Theory, Implementations, and Applications Scalable Quantum Machine Learning: Trainability, Expressivity and Efficiency

Reference 256

Resolution: verified exact; local_arxiv, observed 2026-08-06T00:30:50.931111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.