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Paper Citation Record · LEDGER

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning

As of 21 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2412.06938.

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

pith.paper-citation-record.v1
2412.06938 v1

Coverage vector

measured 54 of 54 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-11T19:27:05.798866Z

measured 54 of 54 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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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Reference resolution

54 of 54 outbound references displayed

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  • unresolved32
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External citation measurements

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Outbound references

Observation 0e8efee0-a415-4ebe-8d26-a6e76b68e7d6 · outbound

This paper cites Wehner, D.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Wehner, D

Reference 1

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Observation 7ee31850-ead6-45ef-a366-94e0464942c2 · outbound

This paper cites an unresolved cited work.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Unresolved cited work

Reference 2

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Observation 919e5b1d-b930-4093-8210-a04df306a80d · outbound

This paper cites an unresolved cited work.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Unresolved cited work

Reference 3

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Observation 22e18bf7-0c2f-4365-b0f7-2c634c8ee414 · outbound

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Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Unresolved cited work

Reference 4

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Observation 2c71556c-613f-4420-849f-414ca44e0120 · outbound

This paper cites Universal blind quantum computation.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Universal blind quantum computation

Reference 5

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Observation 33c37da6-5b29-460e-9fe0-92336b2354b2 · outbound

This paper cites Gottesman, T.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Gottesman, T

Reference 6

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Observation 19894c32-c911-496c-993a-6c8ef1c9c5c2 · outbound

This paper cites Kómár, E.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Kómár, E

Reference 7

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Observation 4838e1f4-edef-437e-8e29-16a5c4140fa0 · outbound

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Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Unresolved cited work

Reference 8

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source=pdf_text observed=2026-08-11T19:27:05.577832Z digest=sha256:8eb69c6f81f656df320f7817d3ff4e29155a873575f7f41989b3dd3b7299a4a6

Observation a6bf0e9f-5d27-4728-bac4-b517ab66a094 · outbound

This paper cites an unresolved cited work.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Unresolved cited work

Reference 9

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Observation 8fffd2fb-4d53-4353-9494-584ddd2f0acb · outbound

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Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Unresolved cited work

Reference 10

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source=pdf_text observed=2026-08-11T19:27:05.587672Z digest=sha256:5cab7a3d2b9502f347c4b7e33bf774241eb1c87ee731f013222e0229ee6fb234

Observation 6af1f385-4dc6-4ba8-9eb5-42eb3b54479c · outbound

This paper cites an unresolved cited work.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Unresolved cited work

Reference 11

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Observation f841f736-1b49-46b7-9171-17702a17381a · outbound

This paper cites Azuma, S.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Azuma, S

Reference 12

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source=pdf_text observed=2026-08-11T19:27:05.597206Z digest=sha256:8a79f550a2f2cf80a2c8503083eb85b32be939410068a95a67f0e1cbc346b1a4

Observation 983d4d23-35a6-40e2-b2a3-8537a04d6d01 · outbound

This paper cites an unresolved cited work.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Unresolved cited work

Reference 13

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Observation 08329922-45a1-4cf6-8d8b-d330d87a31e7 · outbound

This paper cites Event-ready-detectors.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Event-ready-detectors

Reference 14

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Observation faac0975-d626-4dbe-b715-a8747e181d0e · outbound

This paper cites On noise in swap ASAP repeater chains: exact analytics, distributions and tight approximations.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning On noise in swap ASAP repeater chains: exact analytics, distributions and tight approximations

Reference 15

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source=pdf_text observed=2026-08-11T19:27:05.611204Z digest=sha256:867959b2a1c881a7d5f3159fb205554c3576ff60b91fe67f731d48a304d113ac

Observation edaaa206-a130-4c8e-9e0a-d671bab24d94 · outbound

This paper cites Coopmans, S.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Coopmans, S

Reference 16

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Observation 32560899-03f5-41ba-bcbc-6d1876e8f4ab · outbound

This paper cites Kamin, E.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Kamin, E

Reference 17

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Observation 1f1da076-9edc-42af-99b8-4ce02aa6f17c · outbound

This paper cites Entanglement Swapping for Repeater Chains with Finite Memory Sizes.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Entanglement Swapping for Repeater Chains with Finite Memory Sizes

Reference 18

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T19:27:05.625958Z digest=sha256:4efe92e076970c33283bf1640d5e8e9fb018f92120eec8ddf0d12367d41daf9d

Observation 9cfdb57f-00da-4a0c-a0cf-1d81c2d7448d · outbound

This paper cites On the Analysis of Quantum Repeater Chains with Sequential Swaps.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning On the Analysis of Quantum Repeater Chains with Sequential Swaps

Reference 19

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Observation bc654e91-a896-4a8a-851f-2f00d0f3057d · outbound

This paper cites Shchukin and P.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Shchukin and P

Reference 20

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Observation 2055aff2-66c3-46a6-8000-214d43dee948 · outbound

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Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Unresolved cited work

Reference 21

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Observation b41601d1-c5eb-4552-8908-89399aeaa717 · outbound

This paper cites Haldar, P.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Haldar, P

Reference 22

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Observation ea08e650-15d6-4063-a821-dad3a5c6279a · outbound

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Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Unresolved cited work

Reference 23

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Observation 370b9505-deff-49c2-bac3-c06919336662 · outbound

This paper cites Design and demonstration of an operating system for executing applications on quantum network nodes.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Design and demonstration of an operating system for executing applications on quantum network nodes

Reference 24

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Observation fe2066ee-e48f-4120-8232-581fea5fd2df · outbound

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Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Unresolved cited work

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Observation eec5bcf4-9e00-4a09-b431-6485e2e46804 · outbound

This paper cites Rozpędek, K.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Rozpędek, K

Reference 26

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Observation 0097bea0-0e73-425b-894e-5785a76469a6 · outbound

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Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Unresolved cited work

Reference 27

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Observation 395e895c-0b6e-42c3-b1af-937ca9480215 · outbound

This paper cites Reducing classical communication costs in multiplexed quantum repeaters using hardware-aware quasi-local policies.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Reducing classical communication costs in multiplexed quantum repeaters using hardware-aware quasi-local policies

Reference 28

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Observation af294ff2-3118-4d5e-98bb-5ab355adcf81 · outbound

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Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Unresolved cited work

Reference 29

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source=pdf_text observed=2026-08-11T19:27:05.676499Z digest=sha256:8c48bccda4a125ffe24b807d00814de6c9422328976b68d581a9797f43d28d44

Observation a9182efe-061d-466c-bb53-852f848a2959 · outbound

This paper cites Deterministic delivery of remote entanglement on a quantum network.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Deterministic delivery of remote entanglement on a quantum network

Reference 30

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Observation 31339a53-0f06-46ba-b53d-bc326c92dcc2 · outbound

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Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Unresolved cited work

Reference 31

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Observation 83e2ea50-ab16-49f8-a7aa-812e5f0bf295 · outbound

This paper cites Khatri, On the design and analysis of near-term quan- tum network protocols using Markov decision processes, AVS Quantum Science4, 030501 (2022).

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Khatri, On the design and analysis of near-term quan- tum network protocols using Markov decision processes, AVS Quantum Science4, 030501 (2022)

Reference 32

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source=pdf_text observed=2026-08-11T19:27:05.690673Z digest=sha256:431c9c2bd6dc077b6dfc22ce301563fd9bbd00e91903f263a5ef6889f7315f06

Observation b0720a9f-9e60-4992-b717-de7200a31e66 · outbound

This paper cites Shchukin, F.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Shchukin, F

Reference 33

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source=pdf_text observed=2026-08-11T19:27:05.695489Z digest=sha256:e77f21a84579fba2740c372da51d78f40b33bf0cfb3acf2454fe3dd2c10104aa

Observation b05f6202-0fb2-426a-a269-5a55680bd84a · outbound

This paper cites an unresolved cited work.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Unresolved cited work

Reference 34

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source=pdf_text observed=2026-08-11T19:27:05.700198Z digest=sha256:a8bf35de2ddb38a436a8afe382ebb0053ba7512aa0bf98020750e6713ca383d3

Observation dafda167-5c79-4f27-a275-896d2c994869 · outbound

This paper cites Fast and reliable entanglement distribution with quantum repeaters: principles for improving protocols using reinforcement learning.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Fast and reliable entanglement distribution with quantum repeaters: principles for improving protocols using reinforcement learning

Reference 35

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local_arxiv, observed 2026-08-11T19:27:06.029878Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T19:27:05.705119Z digest=sha256:4fb044ae890e89f3f2975b338a5ae42d3496b835620fa718b1921288cda9b78d

Observation e8da3414-440c-4df3-b02b-1cdcd296b7c1 · outbound

This paper cites Coopmans,Tools for the Design of Quantum Repeater Networks, Ph.D.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Coopmans,Tools for the Design of Quantum Repeater Networks, Ph.D

Reference 36

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source=pdf_text observed=2026-08-11T19:27:05.710598Z digest=sha256:956da5e8fdb5aa01353ad7f852a16c66c376ae38eb0cd446039126fdc9b36799

Observation e9f6b940-b5cc-48d4-a900-d81f9839cf3a · outbound

This paper cites Raffin, A.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Raffin, A

Reference 37

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source=pdf_text observed=2026-08-11T19:27:05.715490Z digest=sha256:9219b7a3745e425e0d928e4b2614e9c8b4f0bd99601d8436499ee792153c69a7

Observation 7ca5914e-d9b1-4824-87e8-0f3618f8538a · outbound

This paper cites an unresolved cited work.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Unresolved cited work

Reference 38

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unresolved
raw_fallback, observed 2026-08-11T19:27:06.362007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T19:27:05.720224Z digest=sha256:58959dcb0b6acd4b9599edbdc85e4a7d9e8e9cb6fd45a4b6ea64a320a6696112

Observation 6d472b6d-696d-4f2b-a6d8-5fb768e73ff7 · outbound

This paper cites The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-11T19:27:05.725101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T19:27:05.725101Z digest=sha256:ce3e24c04d4ed01cf04454f113d4d7370d74cd551fe1dd0153fa59712f0d0e9b

Observation 72f7a569-1b5a-415b-ba27-6f86aa677efd · outbound

This paper cites Zhu and X.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Zhu and X

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:27:06.345857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T19:27:05.730142Z digest=sha256:2d6ebbe5dfebb00d7301d9e713bb8d513bd4d71b7a38c9983ae856ef7b257c2d

Observation 1c35b7be-de54-4542-965d-3f2abf76d8c8 · outbound

This paper cites an unresolved cited work.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Unresolved cited work

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-11T19:27:05.735043Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T19:27:05.735043Z digest=sha256:87589c9ecad3beb23e53480698845d5edc2773ea7f7b69c89fb5556653f930f8

Observation 6949d95d-f2ee-49b2-a8dc-0335436384d1 · outbound

This paper cites Tackling Decision Processes with Non-Cumulative Objectives using Reinforcement Learning.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Tackling Decision Processes with Non-Cumulative Objectives using Reinforcement Learning

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-11T19:27:05.739836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T19:27:05.739836Z digest=sha256:27b46eb6a6e5f45e4cc16d36b4d792c0725bf3c7e38110863539b097be76f1a8

Observation 31381f03-6aa6-405c-be7a-ab004c55327b · outbound

This paper cites Nägele and F.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Nägele and F

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:27:06.329759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T19:27:05.744740Z digest=sha256:8f49019f7bf2f35cf2521f01a46ab2e859de807214a4655b672b8ef6f3e6a117

Observation e6fd5bfb-048c-4a7d-bfca-353e5f860d53 · outbound

This paper cites an unresolved cited work.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Unresolved cited work

Reference 44

Resolution
unresolved
raw_fallback, observed 2026-08-11T19:27:06.314361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T19:27:05.749564Z digest=sha256:651ae702f091e260aa69cc01c6582ea176de11ea51f23380cafa3c5c23664027

Observation 66f2117f-dcf8-4c2e-ab27-c2c91c4faf08 · outbound

This paper cites Buşoniu, R.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Buşoniu, R

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:27:06.298403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T19:27:05.754742Z digest=sha256:1979254a20eddcee3bc936b710000b6e2d7bbedd29c7a3b83d46b5efec6d971e

Observation 628cf244-8513-4432-9059-0dd5cb631c04 · outbound

This paper cites Zhang, Z.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Zhang, Z

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:27:06.282639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T19:27:05.760192Z digest=sha256:0d92c209e62ba50c739716769e382270aa87b868321ed0cfd03d1ccca2a57bea

Observation 1d167c58-8946-495d-806b-267325a99b11 · outbound

This paper cites an unresolved cited work.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Unresolved cited work

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-11T19:27:05.765232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T19:27:05.765232Z digest=sha256:a9f846f2451a4297be965efe45af7d64a7723beb0761e1277144cf5d21ef0156

Observation e5294a49-f066-4e36-9dfc-091129829898 · outbound

This paper cites Shahbeigi, D.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Shahbeigi, D

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:27:06.267850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T19:27:05.770326Z digest=sha256:c0d53c7e45c89185ea44bbf0ee2c8d35a27191eedc24e13b4d7aba43eb031b9c

Observation cd70b097-4e47-45de-93df-a376be3c8efe · outbound

This paper cites an unresolved cited work.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Unresolved cited work

Reference 49

Resolution
unresolved
raw_fallback, observed 2026-08-11T19:27:06.251126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T19:27:05.775783Z digest=sha256:dad2ecb6d85b2e4597b2f48c9f08a6b37bc82d81bb78e23b9487925b3720c102

Observation dc48dfac-eaaf-4143-b8e7-0ed9d46bae0a · outbound

This paper cites Here n ∈ N is the number of nodes in the linear network, where each node is labelled by an integer i ∈ [n].

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Here n ∈ N is the number of nodes in the linear network, where each node is labelled by an integer i ∈ [n]

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:27:06.232811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T19:27:05.780296Z digest=sha256:f4ed77f63995312630daea5fe5aaf755e2c944e9e6d43631bf45dadded9e7e5b

Observation d02fe90b-ddd0-40d1-bac2-8c83758ab54e · outbound

This paper cites When the link gen- eration attempt is applied, it first removes all links con- nected to qubits at segmenti, i.e.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning When the link gen- eration attempt is applied, it first removes all links con- nected to qubits at segmenti, i.e

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:27:06.216844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T19:27:05.784841Z digest=sha256:57f9e656ce1e6e23e3a16b3affea17e38ba53eb4575cae4a502ed914a1d303fa

Observation 5f98ef5a-f381-41ed-9dfd-929e3a7aaf1d · outbound

This paper cites In each even round, it will perform a swap on all of the nodes where two links are present.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning In each even round, it will perform a swap on all of the nodes where two links are present

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:27:06.201280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T19:27:05.789912Z digest=sha256:493379577c8e32bf8b69c9c935effdb28406b13b8116141c6378f455fb5a727d

Observation 2c5c80d1-b6a7-43df-b409-366d976b790b · outbound

This paper cites This is to ensure that each time after sending out actions, enough time has passed such that all results have been collected back.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning This is to ensure that each time after sending out actions, enough time has passed such that all results have been collected back

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:27:06.185432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T19:27:05.794036Z digest=sha256:cd97b1984c508d15d5a46fbb41f704061afe2b8822eb62809ef756ad8abc81b7

Observation 9d04a2d5-efe0-4174-9236-add6aa99107f · outbound

This paper cites an unresolved cited work.

Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning Unresolved cited work

Reference 54

Resolution
unresolved
raw_fallback, observed 2026-08-11T19:27:06.168889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-11T19:27:05.798866Z digest=sha256:540f118ebdf3e6c0a0bd0284feb0b79c96ede05c183d3dfee74839e427c0f6a0

Pith citing papers

No inbound Pith citation observations are available.