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Paper Citation Record · LEDGER

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

As of 15 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

Typed states for the displayed outbound observations.

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

measured 54 of 54 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.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

54 of 54 outbound references displayed

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  • verified fuzzy20
  • 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

This paper cites an unresolved cited work.

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

Reference 8

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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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source=pdf_text observed=2026-08-11T19:27:05.582737Z digest=sha256:17c2473e4789f9c6c4ec1b5afab96baf3d541a85a967f5c8d145b8b59dbff460

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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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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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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source=pdf_text observed=2026-08-11T19:27:05.606450Z digest=sha256:0be08fedaeebaff0e9681468db69c732cda1079d86ffd2410192250c2f511a21

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=sha256:676b669b5d9f7c8b14ea6878ea41590f708009fdfedcf9cf9a77722a4b06aadb

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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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

This paper cites an unresolved cited work.

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

Reference 25

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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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source=pdf_text observed=2026-08-11T19:27:05.671689Z digest=sha256:770daf8b01cfa3dae59f9d566d90ef076a2e66a6ffd592df83f958444629adb9

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=sha256:6999952644c79ecb95c11437ac659c0db783b7d4931222faaeef23ae04e2c7de

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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source=pdf_text observed=2026-08-11T19:27:05.681118Z digest=sha256:a05fd15f719d410e91fe3d218c6d7b51f7ee6e34189e59610177de9e77868bd9

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

This paper cites an unresolved cited work.

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=sha256:1e15d8f1df9ad1d6ae646da868535a322c5f0aebc08fa441026a113a9eb8bbe6

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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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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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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T19:27:05.705119Z digest=sha256:d14100d57d30f335e9b519f2b31540cc20980514e6aba2ce40cba429f68e86b2

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=sha256:5cd7c916d6d7c92e8111d04a74a39d43bc60b75395f1e4b7a939888c6a8b26b7

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:d53e5ce22d64af173998ffba5c1ba7b82e0922aee6a29c243d877de4401a9ca2

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

Resolution
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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T19:27:05.720224Z digest=sha256:a09481a528132bd4a399d197589050ac2136261ceb885187eae41f18c969de3a

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=sha256:457040cedebbb39a6f50e583e213de6f62310b8aa0bf72d3d21e6fc7bbbc8ac7

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T19:27:05.730142Z digest=sha256:47b43fb8f5429762659095a0f3fdbf14d94874aab9f9f149341d2b30679bead3

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:b89626e89f59f911897f02b9cd0a99960cc04c0026382679b9201fd120fd01fe

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=sha256:845378bb4cbaf0a7ac5e240c3c45e3d928464d4843cba5c18e4a8abd02f43754

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T19:27:05.744740Z digest=sha256:f54d7b62af835e26c5a2f2e28c0b6fcd817dc5b60d051a8eace3237af6d37871

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T19:27:05.749564Z digest=sha256:94e9a74cbedd692d9cff24e4cafd8e0b4e34a7930a8cce014ab522e70cc651a5

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T19:27:05.754742Z digest=sha256:febc879ec52e313b78f582e861126a0c0b1d98d744fc74191a32324e8b11bb2b

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T19:27:05.760192Z digest=sha256:a607865ea0d65d9c36d5d8c70478bb29526abd7ac265dfb8c016a3eeb19067e6

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=sha256:2cc13c18e95a8faf7d3ac83bf14612431357754c66525a35ce0efd3a17eb3817

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T19:27:05.770326Z digest=sha256:f8299503cec9a52c2c834b185299ccb6dbfe85fd265d25678f4a20e8f75463bd

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T19:27:05.775783Z digest=sha256:224cf31aeb03aa2ecbd22aaaf2ce900e7255a54edbfc6722642b352b3510dc82

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T19:27:05.780296Z digest=sha256:ee29d61e0970ead666a20953f71c5f0a0b7a1a97621dbb5c9de77cb2a8c5d0ed

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T19:27:05.784841Z digest=sha256:ceae275d9215dd1d89ab51306c415733d737acfdccacb86d0ca89dcc31a14ee2

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T19:27:05.789912Z digest=sha256:f869be01f9fc9f6dbbb0b2c4f8de1390094d44641fa513154ed4b281ead653ed

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T19:27:05.794036Z digest=sha256:0a77a0f00d83720b9d9f0454c3a8ea85d748afe97e5250be9f08e56ecfb2f1ed

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T19:27:05.798866Z digest=sha256:a5fa81b7eabb91366318f92a663f98b836683e91cf4afdb8c69a7b551abb7fb5

Pith citing papers

No inbound Pith citation observations are available.