Paper Citation Record · LEDGER

Artificial intelligence for representing and characterizing quantum systems

As of 19 August 2026, this Paper Citation Record lists 100 of 300 outbound references and 7 inbound Pith citation observations for arXiv:2509.04923.

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

pith.paper-citation-record.v1
2509.04923 v1

Coverage vector

measured 100 of 300 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T05:50:39.102300Z

measured 107 of 107 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T19:22:38.309565Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-08T13:04:56.674267Z

Reference resolution

100 of 300 outbound references displayed

verified exact4
verified fuzzy0
unresolved96
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8c72a9b9-0a5d-4a1f-bfff-c8572d2188c7 · outbound

This paper cites Aaronson.

Artificial intelligence for representing and characterizing quantum systems Aaronson

Reference 1

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Observation 4994f6d2-bd85-4d6c-89b0-d886363723cb · outbound

This paper cites Aaronson and D.

Artificial intelligence for representing and characterizing quantum systems Aaronson and D

Reference 2

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Observation 25690e7b-79cc-40e5-b127-95139989b33e · outbound

This paper cites Quantum computing and artificial intelligence: status and perspectives.

Artificial intelligence for representing and characterizing quantum systems Quantum computing and artificial intelligence: status and perspectives

Reference 3

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Observation e3d5aabe-6dd0-4dda-99a9-aaaa704d61e5 · outbound

This paper cites Agrawal, J.

Artificial intelligence for representing and characterizing quantum systems Agrawal, J

Reference 4

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Observation 1c74b7f0-8c12-44e5-9d80-a7872c1bc1e0 · outbound

This paper cites Aharonov, X.

Artificial intelligence for representing and characterizing quantum systems Aharonov, X

Reference 5

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Observation 5a128e7a-f28b-4cb8-8e5e-58a428db50cd · outbound

This paper cites Ahmed, C.

Artificial intelligence for representing and characterizing quantum systems Ahmed, C

Reference 6

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Observation bc430d94-e066-4c3e-a2a5-05e22793ee74 · outbound

This paper cites an unresolved cited work.

Artificial intelligence for representing and characterizing quantum systems Unresolved cited work

Reference 7

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Observation b836ee3e-5dbd-4279-85c3-63874540f52d · outbound

This paper cites Ai and Y .-X.

Artificial intelligence for representing and characterizing quantum systems Ai and Y .-X

Reference 8

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Observation f050625b-d6fa-4143-a393-c48a5e800bf4 · outbound

This paper cites an unresolved cited work.

Artificial intelligence for representing and characterizing quantum systems Unresolved cited work

Reference 9

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Observation de4548d2-fe98-42bf-abef-f88c2b460f5c · outbound

This paper cites Alexeev et al.

Artificial intelligence for representing and characterizing quantum systems Alexeev et al

Reference 10

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Observation 5618d105-c29f-494f-bdd7-8a7476e5e828 · outbound

This paper cites an unresolved cited work.

Artificial intelligence for representing and characterizing quantum systems Unresolved cited work

Reference 11

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Observation 99f2d1f1-f2db-46b7-a460-a1627873e564 · outbound

This paper cites an unresolved cited work.

Artificial intelligence for representing and characterizing quantum systems Unresolved cited work

Reference 12

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Observation a362322a-0a1a-4a18-94a7-d6a1c5ecb4da · outbound

This paper cites an unresolved cited work.

Artificial intelligence for representing and characterizing quantum systems Unresolved cited work

Reference 13

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Observation 3cb75adf-e36c-45c8-a60b-d92430bcfc5a · outbound

This paper cites Anshu and S.

Artificial intelligence for representing and characterizing quantum systems Anshu and S

Reference 14

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Observation 5aabdf07-9fa6-4e9c-8d49-275e983b1085 · outbound

This paper cites Arnold and F.

Artificial intelligence for representing and characterizing quantum systems Arnold and F

Reference 15

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Observation 635e15e6-7b50-4ecd-8565-bf339fa5b506 · outbound

This paper cites Arnold, F.

Artificial intelligence for representing and characterizing quantum systems Arnold, F

Reference 16

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Observation d367983d-4be8-4a67-9bbf-89dbe74e6316 · outbound

This paper cites Arunachalam and R.

Artificial intelligence for representing and characterizing quantum systems Arunachalam and R

Reference 17

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Observation 4250e43f-dd49-49b2-a18e-8b24589bd8a5 · outbound

This paper cites an unresolved cited work.

Artificial intelligence for representing and characterizing quantum systems Unresolved cited work

Reference 18

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Observation da0a051a-bc1a-4f7f-aeb9-9a80652fcc54 · outbound

This paper cites Baltru ˇsaitis, C.

Artificial intelligence for representing and characterizing quantum systems Baltru ˇsaitis, C

Reference 19

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Observation 1c489e6a-460e-4c6b-b108-de3d6aefefad · outbound

This paper cites an unresolved cited work.

Artificial intelligence for representing and characterizing quantum systems Unresolved cited work

Reference 20

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Observation e23cea29-8753-4155-9536-97aa794fa76e · outbound

This paper cites Barratt, U.

Artificial intelligence for representing and characterizing quantum systems Barratt, U

Reference 21

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Observation d274cb70-8c5d-41e1-9889-2ddc3540111f · outbound

This paper cites Bausch et al.

Artificial intelligence for representing and characterizing quantum systems Bausch et al

Reference 22

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Observation 42721f88-bf87-4187-ba1f-38eb985c393c · outbound

This paper cites Begu ˇsi´c, J.

Artificial intelligence for representing and characterizing quantum systems Begu ˇsi´c, J

Reference 23

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Observation f60ba793-76ac-4373-8996-6c6240bf988e · outbound

This paper cites Bengio, A.

Artificial intelligence for representing and characterizing quantum systems Bengio, A

Reference 24

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Observation f0e69dfe-334d-44f5-8106-df23b59887a1 · outbound

This paper cites an unresolved cited work.

Artificial intelligence for representing and characterizing quantum systems Unresolved cited work

Reference 25

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Observation bbc60173-2c55-4705-a513-7dddde9a3568 · outbound

This paper cites Bertoni, J.

Artificial intelligence for representing and characterizing quantum systems Bertoni, J

Reference 26

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Observation 3a4f67a4-0c7e-4e35-a53a-1eeea70e3903 · outbound

This paper cites Bharti et al.

Artificial intelligence for representing and characterizing quantum systems Bharti et al

Reference 27

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Observation c4d71c23-7c43-488d-b724-badcce558ea0 · outbound

This paper cites an unresolved cited work.

Artificial intelligence for representing and characterizing quantum systems Unresolved cited work

Reference 28

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Observation 87e39637-6ef5-409a-96d7-b9ed995b190c · outbound

This paper cites Bluvstein et al.

Artificial intelligence for representing and characterizing quantum systems Bluvstein et al

Reference 29

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Observation ddfcced0-e0ca-4de7-876f-6cd67a6cb421 · outbound

This paper cites Bohrdt, S.

Artificial intelligence for representing and characterizing quantum systems Bohrdt, S

Reference 30

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Observation e20865e6-0ec1-4441-8346-619620345a6f · outbound

This paper cites an unresolved cited work.

Artificial intelligence for representing and characterizing quantum systems Unresolved cited work

Reference 31

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Observation 4a727454-95f4-4856-a648-9633f9570fa2 · outbound

This paper cites Public-key pseudoentanglement and the hardness of learning ground state entanglement structure.

Artificial intelligence for representing and characterizing quantum systems Public-key pseudoentanglement and the hardness of learning ground state entanglement structure

Reference 32

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Observation 84e2492f-d310-4b4d-aabe-da696c4451d5 · outbound

This paper cites On the hardness of learning ground state entanglement of geometrically local Hamiltonians.

Artificial intelligence for representing and characterizing quantum systems On the hardness of learning ground state entanglement of geometrically local Hamiltonians

Reference 33

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Observation e514f020-3291-4683-9674-0be98f17dc66 · outbound

This paper cites Bourgund et al.

Artificial intelligence for representing and characterizing quantum systems Bourgund et al

Reference 34

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Observation 7c965f06-30c2-455a-be44-a78684b3520b · outbound

This paper cites Bravyi and D.

Artificial intelligence for representing and characterizing quantum systems Bravyi and D

Reference 35

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Observation 27b839f9-e105-47d0-96a3-70afb0ee9ac5 · outbound

This paper cites Brown et al.

Artificial intelligence for representing and characterizing quantum systems Brown et al

Reference 36

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Observation 4bf060ca-b2da-4861-82b3-06cd88e5cc77 · outbound

This paper cites an unresolved cited work.

Artificial intelligence for representing and characterizing quantum systems Unresolved cited work

Reference 37

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Observation 1969c32f-daec-4624-8b69-8008d6b74c26 · outbound

This paper cites Campaioli, J.

Artificial intelligence for representing and characterizing quantum systems Campaioli, J

Reference 38

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Observation 62ec2ce7-dd53-4216-9a30-d8737cd7f2b6 · outbound

This paper cites Enhancing LLM-based Quantum Code Generation with Multi-Agent Optimization and Quantum Error Correction.

Artificial intelligence for representing and characterizing quantum systems Enhancing LLM-based Quantum Code Generation with Multi-Agent Optimization and Quantum Error Correction

Reference 39

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Observation d8dfb6f5-b888-46c9-ae4e-4bff05e7957c · outbound

This paper cites qecGPT: decoding Quantum Error-correcting Codes with Generative Pre-trained Transformers.

Artificial intelligence for representing and characterizing quantum systems qecGPT: decoding Quantum Error-correcting Codes with Generative Pre-trained Transformers

Reference 40

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Observation 7b3012af-d5a5-46bb-895d-fac810018437 · outbound

This paper cites an unresolved cited work.

Artificial intelligence for representing and characterizing quantum systems Unresolved cited work

Reference 41

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Observation 22f31255-b2e3-410f-8133-b14fa367cb11 · outbound

This paper cites A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT.

Artificial intelligence for representing and characterizing quantum systems A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT

Reference 42

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Observation e70ba40b-19bb-4886-93de-77efbbe8f304 · outbound

This paper cites Carleo, I.

Artificial intelligence for representing and characterizing quantum systems Carleo, I

Reference 43

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Observation a8be85a8-b344-4dc9-8af8-d05dfab81ace · outbound

This paper cites Carleo and M.

Artificial intelligence for representing and characterizing quantum systems Carleo and M

Reference 44

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Observation 8c8e17d1-5a79-4ab4-8036-d346054b0148 · outbound

This paper cites Carrasco, A.

Artificial intelligence for representing and characterizing quantum systems Carrasco, A

Reference 45

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Observation 27aee7d9-1479-4c12-a653-7da7e70be8ab · outbound

This paper cites Carrasquilla.

Artificial intelligence for representing and characterizing quantum systems Carrasquilla

Reference 46

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Observation 11332b2d-489a-400c-9a1c-992339ba828a · outbound

This paper cites Carrasquilla and R.

Artificial intelligence for representing and characterizing quantum systems Carrasquilla and R

Reference 47

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Observation 2baec11c-d8b0-4869-8688-c41380b5aa41 · outbound

This paper cites Carrasquilla and G.

Artificial intelligence for representing and characterizing quantum systems Carrasquilla and G

Reference 48

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Observation 875cade7-fbbb-4f6d-ab7a-8e19c324ed00 · outbound

This paper cites Carrasquilla, G.

Artificial intelligence for representing and characterizing quantum systems Carrasquilla, G

Reference 49

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Observation 7c426c26-3939-47d0-a0fd-92338c9aa4b1 · outbound

This paper cites Cerezo et al.

Artificial intelligence for representing and characterizing quantum systems Cerezo et al

Reference 50

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Observation 60601159-ffd9-4966-9e32-f79bc5ab8d63 · outbound

This paper cites Does provable absence of barren plateaus imply classical simulability?.

Artificial intelligence for representing and characterizing quantum systems Does provable absence of barren plateaus imply classical simulability?

Reference 51

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Observation 1a51a125-fb3a-4d11-ae8e-4a9b5b211ad1 · outbound

This paper cites Cerezo, M.

Artificial intelligence for representing and characterizing quantum systems Cerezo, M

Reference 52

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Observation be7b687e-f87c-44ff-b092-ce98002e1289 · outbound

This paper cites Cervera-Lierta, J.

Artificial intelligence for representing and characterizing quantum systems Cervera-Lierta, J

Reference 53

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Observation 96c38fb9-1b53-4bba-8419-de9d300a4aa2 · outbound

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Artificial intelligence for representing and characterizing quantum systems Unresolved cited work

Reference 54

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Observation 2986666e-ab47-43d1-9606-336c0ac3fabc · outbound

This paper cites Chang et al.

Artificial intelligence for representing and characterizing quantum systems Chang et al

Reference 55

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Observation 853bf256-0985-4397-8363-db818e3fcccf · outbound

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Artificial intelligence for representing and characterizing quantum systems Unresolved cited work

Reference 56

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Observation 7236da92-aea9-45ff-84a6-a720ae919173 · outbound

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Artificial intelligence for representing and characterizing quantum systems Unresolved cited work

Reference 57

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Observation 3cccef48-b199-4121-bf9b-11396dc2b906 · outbound

This paper cites Quantum circuit complexity and unsupervised machine learning of topological order.

Artificial intelligence for representing and characterizing quantum systems Quantum circuit complexity and unsupervised machine learning of topological order

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-08-05T05:50:41.514020Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 88ce7ee3-745b-4515-bba6-966f951e62fd · outbound

This paper cites Chen and M.

Artificial intelligence for representing and characterizing quantum systems Chen and M

Reference 59

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Observation b8200be3-6aa9-4ac7-9ebf-25a5a76a8a8c · outbound

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Artificial intelligence for representing and characterizing quantum systems Unresolved cited work

Reference 60

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Observation 96dd96f9-f187-427f-b727-2a98eaa91015 · outbound

This paper cites an unresolved cited work.

Artificial intelligence for representing and characterizing quantum systems Unresolved cited work

Reference 61

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Observation 7b2a9ca8-9e85-43eb-8333-67b40a3bb6ab · outbound

This paper cites Predicting quantum channels over general product distributions.

Artificial intelligence for representing and characterizing quantum systems Predicting quantum channels over general product distributions

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-08-05T05:50:41.492593Z

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Observation 4774a007-ba6d-486c-806e-2d4da8383bc6 · outbound

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Artificial intelligence for representing and characterizing quantum systems Unresolved cited work

Reference 63

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Observation 6a00f249-be18-4170-a7b3-058c3a7ee0c9 · outbound

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Artificial intelligence for representing and characterizing quantum systems Unresolved cited work

Reference 64

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Observation e22dfc0d-03cb-4647-a4ad-51665ccb0ca3 · outbound

This paper cites Cho and D.

Artificial intelligence for representing and characterizing quantum systems Cho and D

Reference 65

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Observation 3a99712f-e06e-49d9-922f-7e100098c06c · outbound

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Artificial intelligence for representing and characterizing quantum systems Unresolved cited work

Reference 66

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Observation e50bf65b-e144-453d-bc2c-b6e9fd076f3f · outbound

This paper cites Cimini, M.

Artificial intelligence for representing and characterizing quantum systems Cimini, M

Reference 67

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Observation 1678cf5a-d434-476d-83eb-53400e6852ca · outbound

This paper cites an unresolved cited work.

Artificial intelligence for representing and characterizing quantum systems Unresolved cited work

Reference 68

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Artificial intelligence for representing and characterizing quantum systems A Fourier analysis framework for approximate classical simulations of quantum circuits

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Observation a1fcd35b-97cb-4ebd-82c9-d1af0be30e3d · outbound

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Artificial intelligence for representing and characterizing quantum systems Cramer, M

Reference 70

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Artificial intelligence for representing and characterizing quantum systems Cybi ´nski, M

Reference 71

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Artificial intelligence for representing and characterizing quantum systems Czarnik, A

Reference 72

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Artificial intelligence for representing and characterizing quantum systems Improving the efficiency of learning-based error mitigation

Reference 73

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Artificial intelligence for representing and characterizing quantum systems Czischek, M

Reference 74

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Artificial intelligence for representing and characterizing quantum systems Dawid, J

Reference 75

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Artificial intelligence for representing and characterizing quantum systems de Schoulepnikoff, G

Reference 76

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Artificial intelligence for representing and characterizing quantum systems The computational power of random quantum circuits in arbitrary geometries

Reference 77

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Artificial intelligence for representing and characterizing quantum systems Dehghani, A

Reference 78

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Artificial intelligence for representing and characterizing quantum systems Denis, F

Reference 79

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Artificial intelligence for representing and characterizing quantum systems Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers

Reference 80

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Artificial intelligence for representing and characterizing quantum systems Du, M.-H

Reference 81

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Observation ef89261d-a84c-4b12-b92f-65c4b4c23623 · outbound

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Artificial intelligence for representing and characterizing quantum systems ShadowNet for Data-Centric Quantum System Learning

Reference 82

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Observation c9a72058-c971-4f0e-9603-4cd9628798d3 · outbound

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Artificial intelligence for representing and characterizing quantum systems Unresolved cited work

Reference 83

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Observation 76cd3713-0c7d-4b74-bc2f-51b9a4b78979 · outbound

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Artificial intelligence for representing and characterizing quantum systems Dupuis, L

Reference 84

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Observation a2d5d8c6-aad6-4f3c-b476-0b9acccecbff · outbound

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Artificial intelligence for representing and characterizing quantum systems Eisert, M

Reference 85

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Observation 75345cfc-5f67-4d97-bfc8-6d33762c87c5 · outbound

This paper cites Eisert, D.

Artificial intelligence for representing and characterizing quantum systems Eisert, D

Reference 86

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Observation a4d82bd4-bb35-441b-91a2-4eb8c37d3f44 · outbound

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Artificial intelligence for representing and characterizing quantum systems Elben et al

Reference 87

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Observation 518acd02-6a72-4207-922c-a8c34bbbe117 · outbound

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Artificial intelligence for representing and characterizing quantum systems Elben, S

Reference 88

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Artificial intelligence for representing and characterizing quantum systems RydbergGPT

Reference 89

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Artificial intelligence for representing and characterizing quantum systems Flam-Shepherd, T

Reference 90

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Observation 8cf6126a-7d4f-4ad8-8c27-475de812dc54 · outbound

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Artificial intelligence for representing and characterizing quantum systems Unresolved cited work

Reference 91

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Observation d1aeb56c-35c9-473b-887f-2f23b6830fe6 · outbound

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Artificial intelligence for representing and characterizing quantum systems Classical simulations of noisy variational quantum circuits

Reference 92

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Observation e452fce4-8fdc-4a5d-95e5-c9fca34807f0 · outbound

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Artificial intelligence for representing and characterizing quantum systems Quantum circuit optimization with deep reinforcement learning

Reference 93

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Observation 87d8a8d2-074a-4160-b953-7ef1d4bdcc43 · outbound

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Artificial intelligence for representing and characterizing quantum systems Friedrich and J

Reference 94

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Observation c0f419d6-02d9-47a2-8cb2-85f28ece3156 · outbound

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Artificial intelligence for representing and characterizing quantum systems Frohnert and E

Reference 95

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Artificial intelligence for representing and characterizing quantum systems F ¨urrutter, G

Reference 96

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Artificial intelligence for representing and characterizing quantum systems Concept learning of parameterized quantum models from limited measurements

Reference 97

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Observation d6f5b172-242c-493f-ae8c-f06a103b8956 · outbound

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Artificial intelligence for representing and characterizing quantum systems Gao et al

Reference 98

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Artificial intelligence for representing and characterizing quantum systems Gao et al

Reference 99

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Observation 086d4cd8-4980-47f5-9029-3a370d4adb29 · outbound

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Artificial intelligence for representing and characterizing quantum systems Gao and L.-M

Reference 100

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Pith citing papers

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Data-Driven Learnability Transition of Measurement-Induced Entanglement cites this paper.

Data-Driven Learnability Transition of Measurement-Induced Entanglement Artificial intelligence for representing and characterizing quantum systems

Reference 56

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Spectral Decimation of Quantum Many-Body Hamiltonians Artificial intelligence for representing and characterizing quantum systems

Reference 3

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Comparing Classical Simulation and Sample-Based Learning of Quantum Systems Artificial intelligence for representing and characterizing quantum systems

Reference 9

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Observation e6aa5bda-5fc3-4995-89ff-3d3f9d9c7cf7 · inbound

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Comparing Classical Simulation and Sample-Based Learning of Quantum Systems Artificial intelligence for representing and characterizing quantum systems

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Learning to Reconstruct Wigner Functions in Phase Space Artificial intelligence for representing and characterizing quantum systems

Reference 30

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Observation ae499f15-aa64-4836-9247-454f2f2580a2 · inbound

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Learning Topological Quantum Phases from Limited Subsystems Artificial intelligence for representing and characterizing quantum systems

Reference 36

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OmniQEC: discovering practical quantum error-correcting codes by an AI scientist Artificial intelligence for representing and characterizing quantum systems

Reference 34

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