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Paper Citation Record · LEDGER

Quantum reinforcement learning in dynamic environments

As of 16 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 2 inbound Pith citation observations for arXiv:2507.01691.

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

pith.paper-citation-record.v1
2507.01691 v1

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:53:53.890488Z

measured 63 of 63 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T13:36:11.767803Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T04:53:58.087145Z

Reference resolution

61 of 61 outbound references displayed

  • verified exact13
  • verified fuzzy10
  • unresolved35
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 353c3696-d164-4c89-a8d3-bc72eeb1ad18 · outbound

This paper cites , Barreto , A.

Quantum reinforcement learning in dynamic environments , Barreto , A

Reference 1

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raw_fallback, observed 2026-08-06T20:53:58.165576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T20:53:49.204165Z digest=sha256:b001e634fbf7ed85b1b053ac7c1747b26d6a0fd2a0b1abab2924d3d33d1314b9

Observation 955973a7-3e84-4d5b-b378-136d2f67e920 · outbound

This paper cites , Brassard , G.

Quantum reinforcement learning in dynamic environments , Brassard , G

Reference 2

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no resolver link, observed 2026-08-06T20:53:49.263694Z

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source=arxiv_source observed=2026-08-06T20:53:49.263694Z digest=sha256:c5dd45271a107e23295beb5bb3e3c454d32eafa818f514e32674434a3ba8263f

Observation 14461d0e-3afb-420c-ad4c-4cfde6f6478e · outbound

This paper cites , H yer , P.

Quantum reinforcement learning in dynamic environments , H yer , P

Reference 3

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source=arxiv_source observed=2026-08-06T20:53:49.344854Z digest=sha256:79c868f4d7daf755051707f984035a10ea558309afbb1458edf5953998540026

Observation cc62b7b6-5f73-44e8-a0d3-e069e273b93c · outbound

This paper cites , Cuevas , G.

Quantum reinforcement learning in dynamic environments , Cuevas , G

Reference 4

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doi, observed 2026-08-06T20:53:55.633462Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T20:53:49.430788Z digest=sha256:495b845f9240c663a921ca876794fc86c15b472ccde1ba3e00fee842bfdc5d08

Observation 2237459c-c656-48c7-ae24-14242d3920ac · outbound

This paper cites , Arrasmith , A.

Quantum reinforcement learning in dynamic environments , Arrasmith , A

Reference 5

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source=arxiv_source observed=2026-08-06T20:53:49.498052Z digest=sha256:d26b363596f1791b21ee8b01e8ca3545b2015cd5b97699eea5cda7d4edba64ec

Observation 029b77e2-f599-4105-8035-db0a7f97ca51 · outbound

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

Quantum reinforcement learning in dynamic environments Does provable absence of barren plateaus imply classical simulability?

Reference 6

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source=arxiv_source observed=2026-08-06T20:53:49.562993Z digest=sha256:57c4ea73ea0ce748dea3f1919745b77614953c6bae488266a0c506b4cc3b42dd

Observation d3744512-e590-4679-a5cd-47e66fc515e7 · outbound

This paper cites , Yang , C.-H.H.

Quantum reinforcement learning in dynamic environments , Yang , C.-H.H

Reference 7

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source=arxiv_source observed=2026-08-06T20:53:49.616688Z digest=sha256:8fca86061bfff820f7a1857bfa3de73674436bec135ecb164c42a666a4d2595e

Observation ef5573ed-becf-4741-a15e-2370e3302ac0 · outbound

This paper cites , Chang , Y.-J.

Quantum reinforcement learning in dynamic environments , Chang , Y.-J

Reference 8

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source=arxiv_source observed=2026-08-06T20:53:49.683869Z digest=sha256:4fda9977d72e36fe5c50e08bb1d6a23a6e67dafc6c8d81f14bf16f7cf3f37a3d

Observation 7ab6526e-5e7d-44f3-9d89-b72f76b889d2 · outbound

This paper cites , Kerenidis , I.

Quantum reinforcement learning in dynamic environments , Kerenidis , I

Reference 9

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verified exact
doi, observed 2026-08-06T20:53:55.486535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T20:53:49.742903Z digest=sha256:7ac7ff891741825506fd70f3731148862772bf1a882db87751e4c6c162bb56cd

Observation afad38c7-5e8b-4387-9a56-bc120ceefb7c · outbound

This paper cites , Yeung , D.-Y.

Quantum reinforcement learning in dynamic environments , Yeung , D.-Y

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T20:53:49.812620Z digest=sha256:d006ce1e05215bac6b2364c976235c6a29aaf975fb3d3d3d7bd94cdbec561ae6

Observation 6c20d084-571f-4c46-9916-d60d1ffd07e6 · outbound

This paper cites , Rocchetto , A.

Quantum reinforcement learning in dynamic environments , Rocchetto , A

Reference 11

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source=arxiv_source observed=2026-08-06T20:53:49.892361Z digest=sha256:6054f90d3a488805bab8584d0440637e12583745a8d0e8d22c8c3bf3d5dc8c1f

Observation 62c5b979-489f-4188-baac-00c9f1e5b65e · outbound

This paper cites , Meier , U.

Quantum reinforcement learning in dynamic environments , Meier , U

Reference 12

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source=arxiv_source observed=2026-08-06T20:53:49.947876Z digest=sha256:d9a67cf2659902034dbfe6147e7c4ae5fef568b5ee2fb4a313c6635a56dcb118

Observation b4bbc6a0-0edf-4414-abba-69a8b0244296 · outbound

This paper cites , Buffoni , L.

Quantum reinforcement learning in dynamic environments , Buffoni , L

Reference 13

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source=arxiv_source observed=2026-08-06T20:53:50.027441Z digest=sha256:9c74ceb87fd5aed9184cfe35bc64b4deabfbd98ef56259c4e14b869dfe0032bb

Observation c3212aff-df09-45b1-9da5-5d0d1fab3590 · outbound

This paper cites , Chen , C.

Quantum reinforcement learning in dynamic environments , Chen , C

Reference 14

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no resolver link, observed 2026-08-06T20:53:50.106694Z

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source=arxiv_source observed=2026-08-06T20:53:50.106694Z digest=sha256:00eea9d38f06eab4d6bb049859f0e2043b4ec78cd65db28830b50b26d687135b

Observation 782a178f-564d-413f-b78a-09656ed33ad7 · outbound

This paper cites , Taylor , J.M.

Quantum reinforcement learning in dynamic environments , Taylor , J.M

Reference 15

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source=arxiv_source observed=2026-08-06T20:53:50.158504Z digest=sha256:01e0e0dbde20934a3e1d3aa2e5386b8c8a7f2ffea58e9696e1280b9b5f2fbe82

Observation 44f38714-7a9a-4ea5-a902-9f374b1535ab · outbound

This paper cites Exponential improvements for quantum-accessible reinforcement learning.

Quantum reinforcement learning in dynamic environments Exponential improvements for quantum-accessible reinforcement learning

Reference 16

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local_arxiv, observed 2026-08-06T20:53:56.660236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T20:53:50.229737Z digest=sha256:28051fb7e93dd199c68d9231a29797bceaed14888cbff55f7c906a31aa080f52

Observation b0451e22-e228-41b0-8969-b9c5be9c7662 · outbound

This paper cites , Abbeel , P.

Quantum reinforcement learning in dynamic environments , Abbeel , P

Reference 17

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raw_fallback, observed 2026-08-06T20:53:57.868746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T20:53:50.296449Z digest=sha256:0ff2b26da3847d654bb6dc62d90df13d30f63dc606aabc9ab74426f82d107fa2

Observation db3d71df-13c9-46a0-97c4-0eccd66cd76e · outbound

This paper cites Quantum Computing Provides Exponential Regret Improvement in Episodic Reinforcement Learning.

Quantum reinforcement learning in dynamic environments Quantum Computing Provides Exponential Regret Improvement in Episodic Reinforcement Learning

Reference 18

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local_arxiv, observed 2026-08-06T20:53:56.535115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T20:53:50.355747Z digest=sha256:811dd2405b3b15eb3e5a3319d7e90e22a8acf8a0604929db75b442b775fe707f

Observation 2edad1c7-b56f-42f5-8573-ca0a1eccbb5f · outbound

This paper cites On the relation between trainability and dequantization of variational quantum learning models.

Quantum reinforcement learning in dynamic environments On the relation between trainability and dequantization of variational quantum learning models

Reference 19

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source=arxiv_source observed=2026-08-06T20:53:50.410029Z digest=sha256:28cd1cfabb9bb52623c9f3cf272a882d3fc4a849362bd993d8cb1ed610546763

Observation 46f4b87a-7b86-4aa0-8164-b2f002be4c5a · outbound

This paper cites : Quantum mechanics helps in searching for a needle in a haystack.

Quantum reinforcement learning in dynamic environments : Quantum mechanics helps in searching for a needle in a haystack

Reference 20

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source=arxiv_source observed=2026-08-06T20:53:50.479436Z digest=sha256:77b3444f85e878a76cd82f3391a47c35a90a91a4878522e33e5dd6aeb0f99025

Observation b3643d83-f85b-4755-9767-336d5e1a075a · outbound

This paper cites , Dunjko , V.

Quantum reinforcement learning in dynamic environments , Dunjko , V

Reference 21

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doi, observed 2026-08-06T20:53:55.347003Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T20:53:50.535114Z digest=sha256:49c655cddfd916a27511b782f157eff31d99bef9cafba3c2dc5db33167f3715f

Observation d7c9e87c-d743-407c-9e3a-c8db73e00879 · outbound

This paper cites , W \"o lk , S.

Quantum reinforcement learning in dynamic environments , W \"o lk , S

Reference 22

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doi, observed 2026-08-06T20:53:55.198241Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T20:53:50.594674Z digest=sha256:cf06b698eefe8388d6920cc02a33a0fb4b2be28c8d0e2612812e54da59321c43

Observation 78d4e288-6e8b-4827-a920-2b06ef1336fd · outbound

This paper cites , Gyurik , C.

Quantum reinforcement learning in dynamic environments , Gyurik , C

Reference 23

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raw_fallback, observed 2026-08-06T20:53:57.712455Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T20:53:50.659248Z digest=sha256:3764328961866f64ff52b1686dc30c76122c46afe750f9e04001d6b809c0599c

Observation 6d5184e5-4433-4925-8af7-488cc76fa548 · outbound

This paper cites , Trenkwalder , L.M.

Quantum reinforcement learning in dynamic environments , Trenkwalder , L.M

Reference 24

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source=arxiv_source observed=2026-08-06T20:53:50.734170Z digest=sha256:bb0457f9b2625548a3ec436d5f4a98e9f136f0f652350f106c84cc8e3b21feea

Observation 6bef48a9-503d-44ff-bee1-3dc25f646107 · outbound

This paper cites , Riemer , M.

Quantum reinforcement learning in dynamic environments , Riemer , M

Reference 25

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source=arxiv_source observed=2026-08-06T20:53:50.799756Z digest=sha256:f33a21f172247ee526c4747cd65ffeb7f7d61dde8570ad1307013522c36c02ed

Observation 1674c910-c3aa-4f88-b66a-003d4c04dfb9 · outbound

This paper cites , Sutskever , I.

Quantum reinforcement learning in dynamic environments , Sutskever , I

Reference 26

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source=arxiv_source observed=2026-08-06T20:53:50.884301Z digest=sha256:5469c017990b47a26af74ced138ac064399f91762f8a06dd461fceca2d9bb0bd

Observation d13f9c2f-48eb-4b7c-bcfb-eb1d5920cfda · outbound

This paper cites Gymnasium: A Standard Interface for Reinforcement Learning Environments.

Quantum reinforcement learning in dynamic environments Gymnasium: A Standard Interface for Reinforcement Learning Environments

Reference 27

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source=arxiv_source observed=2026-08-06T20:53:50.948259Z digest=sha256:5a68fe202928177602274c5fd70bda7dcb499714ef392e8ba8a483e0d9299776

Observation e95f4ef8-8896-4499-b714-9d3dbe563874 · outbound

This paper cites Continuous control with deep reinforcement learning.

Quantum reinforcement learning in dynamic environments Continuous control with deep reinforcement learning

Reference 28

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source=arxiv_source observed=2026-08-06T20:53:51.000022Z digest=sha256:20431a1c4013cc57c7bbdf8ead38bd8495a1a7d1aa36e19143c39108f45540e2

Observation 7f5fda6e-27e9-439d-9faf-cd33575f6701 · outbound

This paper cites , Arunachalam , S.

Quantum reinforcement learning in dynamic environments , Arunachalam , S

Reference 29

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source=arxiv_source observed=2026-08-06T20:53:51.052747Z digest=sha256:5b286303ab4072a12fd58fc2fc922f599ba8ed054302e89645cffb8d8b60db53

Observation c2dc4a57-ef15-4a3d-8450-b5bada63572c · outbound

This paper cites , Si , M.

Quantum reinforcement learning in dynamic environments , Si , M

Reference 30

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T20:53:51.141244Z digest=sha256:11840d33b440c08f742f6b289d4729941270a35c2009dede3d6e1e2f13c8f1fb

Observation 9e60453b-79d8-4546-b0d9-cd61ff66bfa5 · outbound

This paper cites , Si , M.

Quantum reinforcement learning in dynamic environments , Si , M

Reference 31

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no resolver link, observed 2026-08-06T20:53:51.215843Z

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source=arxiv_source observed=2026-08-06T20:53:51.215843Z digest=sha256:61ed784e3a61b6f7583f60c993c220f74e314818d29342363a771d623eb6511c

Observation a330f1a2-7879-4b16-84de-a7f45176f5fa · outbound

This paper cites , Makmal , A.

Quantum reinforcement learning in dynamic environments , Makmal , A

Reference 32

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doi, observed 2026-08-06T20:53:54.877334Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T20:53:51.275869Z digest=sha256:d7c12f4241f3c8fde96917c956d4e8167efce081f0958a67450d6ce4a4e23fed

Observation 5410c872-e49b-4dc2-b2fe-1e20b5c1ed80 · outbound

This paper cites , Cohen , N.J.

Quantum reinforcement learning in dynamic environments , Cohen , N.J

Reference 33

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source=arxiv_source observed=2026-08-06T20:53:51.353953Z digest=sha256:cf71333bdaae3516d947da6432e01a3164cbb74e2ff849879a31558abc600aea

Observation e99eca3c-c72c-425a-a24a-704bcf214ea5 · outbound

This paper cites , Makmal , A.

Quantum reinforcement learning in dynamic environments , Makmal , A

Reference 34

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raw_fallback, observed 2026-08-06T20:53:56.386391Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T20:53:51.423143Z digest=sha256:ee80b12e13bb643050e33390eb114f972c53d1a2913c8be09c45c08505524ef1

Observation 60b8c208-528c-4de4-b8d4-b7569320cf65 · outbound

This paper cites , Scherer , D.D.

Quantum reinforcement learning in dynamic environments , Scherer , D.D

Reference 35

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source=arxiv_source observed=2026-08-06T20:53:51.479576Z digest=sha256:0d816dad9933f1e3f3af165cc9c19caeea04c80674366bf434538f7bf04680fe

Observation 0ee2d3a6-cb7f-4f35-9a87-55a4a4924d4f · outbound

This paper cites , Kavukcuoglu , K.

Quantum reinforcement learning in dynamic environments , Kavukcuoglu , K

Reference 36

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no resolver link, observed 2026-08-06T20:53:51.565011Z

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source=arxiv_source observed=2026-08-06T20:53:51.565011Z digest=sha256:1cdc2d50e1ca4c764ea141ded2ce60b51b935ca78b068c1dcb061dab416849d8

Observation f436015a-a989-4c25-9557-b481d162b133 · outbound

This paper cites Exponential quantum advantages in learning quantum observables from classical data.

Quantum reinforcement learning in dynamic environments Exponential quantum advantages in learning quantum observables from classical data

Reference 37

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source=arxiv_source observed=2026-08-06T20:53:51.652640Z digest=sha256:b6395035f546b6d08b84f9fb96e6f91a35e0ebf91474e96809b796c9934cebd0

Observation 75c4d79f-5ecc-491a-8033-a5cb7c0a2c39 · outbound

This paper cites : A survey of reinforcement learning algorithms for dynamically varying environments.

Quantum reinforcement learning in dynamic environments : A survey of reinforcement learning algorithms for dynamically varying environments

Reference 38

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source=arxiv_source observed=2026-08-06T20:53:51.725748Z digest=sha256:723c676f42bf6025502284daf7091244369eab62eca6ffd9ce4698d202de7d47

Observation 62f7310b-4f02-4d29-be8e-6a6d63b000b5 · outbound

This paper cites , Dunjko , V.

Quantum reinforcement learning in dynamic environments , Dunjko , V

Reference 39

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verified exact
doi, observed 2026-08-06T20:53:54.668985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T20:53:51.787914Z digest=sha256:fe832efd7ee26929f90352cf3bd7a665da46a0623fb704fbb793942d1af8cb45

Observation 86015ffb-5129-418a-ab5f-49dde5ad5e7d · outbound

This paper cites , Wiering , M.A.

Quantum reinforcement learning in dynamic environments , Wiering , M.A

Reference 40

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raw_fallback, observed 2026-08-06T20:53:56.044520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T20:53:51.867799Z digest=sha256:2637955dfd2a066d496cb004072bee7a36db5d216dfe7071a6df6cc480b8d4fe

Observation ce160d58-29aa-43de-80f4-9384b9087661 · outbound

This paper cites : Quantum computing in the NISQ era and beyond.

Quantum reinforcement learning in dynamic environments : Quantum computing in the NISQ era and beyond

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-06T20:53:51.931627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:53:51.931627Z digest=sha256:db789b7f797a471d348d2c39693bf8b937d144a8a26d5d9b10e8a776c4242564

Observation e4eed36f-c9ed-4f26-9521-4ec8782e9f28 · outbound

This paper cites : Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1st edn.

Quantum reinforcement learning in dynamic environments : Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1st edn

Reference 42

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verified fuzzy
raw_fallback, observed 2026-08-06T20:53:57.423926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T20:53:52.006742Z digest=sha256:b39fa07929b92c133f1c685f22addf1a1a358e7800a73dff60dc0f73a5b6c1c1

Observation f3c39c8b-79e7-4965-a2ed-312a537115ef · outbound

This paper cites : Continual learning in reinforcement environments.

Quantum reinforcement learning in dynamic environments : Continual learning in reinforcement environments

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:53:57.304850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T20:53:52.089925Z digest=sha256:a68daffe743a2ca4870da46078026300bbd70b5fcc5b4ee2fa7873d02fe66fdf

Observation 8e3e1632-3a8a-4d89-894e-15a5fef20b11 · outbound

This paper cites o mberg , T. , Schiansky , P. , Dunjko , V. , Friis , N. , Harris , N.C. , Hochberg , M. , Englund , D. , W \.

Quantum reinforcement learning in dynamic environments o mberg , T. , Schiansky , P. , Dunjko , V. , Friis , N. , Harris , N.C. , Hochberg , M. , Englund , D. , W \

Reference 44

Resolution
verified exact
doi, observed 2026-08-06T20:53:54.459517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T20:53:52.157831Z digest=sha256:704fcdbe40216b0e9e23d87c49a2484f575cc96adca6c37a0de720eb6a91b546

Observation e85885c9-174d-49bb-84d1-24f6ec28ff85 · outbound

This paper cites , Antonoglou , I.

Quantum reinforcement learning in dynamic environments , Antonoglou , I

Reference 45

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unresolved
no resolver link, observed 2026-08-06T20:53:52.226743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:53:52.226743Z digest=sha256:4247c21b809d4c8c6d3e9edb6e4f6f16629438ef1f1f3ee358be31b42346b112

Observation 6388860c-6b02-4516-8ceb-2979e41349ac · outbound

This paper cites , Killoran , N.

Quantum reinforcement learning in dynamic environments , Killoran , N

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-06T20:53:52.336904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:53:52.336904Z digest=sha256:002ca6b60b14ccbde12e76630dcf58cdc1e2051c04c618e6333ed17f3330c5bc

Observation 0c9ae239-994e-48b8-a92a-f0e2da881216 · outbound

This paper cites , W \"o lk , S.

Quantum reinforcement learning in dynamic environments , W \"o lk , S

Reference 47

Resolution
verified exact
doi, observed 2026-08-06T20:53:54.332661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T20:53:52.432616Z digest=sha256:67e5ef0e5ff1da1d3c7f8d0ba60e7436bdd50c9aa970dcf4de008cc5c128a07b

Observation 0df5869b-0779-4bf7-8b0d-1bad1f827bdc · outbound

This paper cites : Algorithms for quantum computation: discrete logarithms and factoring.

Quantum reinforcement learning in dynamic environments : Algorithms for quantum computation: discrete logarithms and factoring

Reference 48

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unresolved
no resolver link, observed 2026-08-06T20:53:52.527616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:53:52.527616Z digest=sha256:63cfb4d3858b203b8a405235ec198de81804fa7d175d54e6aee92465ca38f271

Observation 78abdd38-7987-4232-96df-342d26cc93fc · outbound

This paper cites , Basso , E.W.

Quantum reinforcement learning in dynamic environments , Basso , E.W

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-06T20:53:52.598401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:53:52.598401Z digest=sha256:f3fe02fd6d035cfd6d5700665dd03395c11d4de64ca0e65afbae182b638473fe

Observation e7c445af-f3d1-490a-a108-1d87e2f1ebaf · outbound

This paper cites , Jerbi , S.

Quantum reinforcement learning in dynamic environments , Jerbi , S

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-06T20:53:52.682047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:53:52.682047Z digest=sha256:a6c5aa1a6841e51e2c22da4dce14f649f225352803a874aa797fecc799c56e22

Observation 4e6be170-b738-4e3c-9b5f-c927b012d8a7 · outbound

This paper cites , Weiss , E.

Quantum reinforcement learning in dynamic environments , Weiss , E

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:53:57.168486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T20:53:52.843181Z digest=sha256:35579b08eec5c05baf787e8e1163302ca010d05c25d60f2d73ca6ad42aa60418

Observation 59147d27-28a7-4caf-9de9-cef62352dade · outbound

This paper cites , W \"o lk , S.

Quantum reinforcement learning in dynamic environments , W \"o lk , S

Reference 52

Resolution
verified exact
doi, observed 2026-08-06T20:53:54.199744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T20:53:52.964122Z digest=sha256:2d5c4d2fd36e91307362c2a1088b34a790a98c3d19d7fc9e6e9f47d6ece13114

Observation aca432a3-74e6-48a4-96d6-6bda11498163 · outbound

This paper cites , Barto , A.G.

Quantum reinforcement learning in dynamic environments , Barto , A.G

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:53:57.029628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T20:53:53.125676Z digest=sha256:7601f80a710df8ca45f9bc3f399e012f4b759174769ec281d4320f4a5c8e5b12

Observation 7d8c3af7-7cf0-4305-b35d-397c0a360eb9 · outbound

This paper cites , Shazeer , N.

Quantum reinforcement learning in dynamic environments , Shazeer , N

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:53:56.907107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T20:53:53.264533Z digest=sha256:0cb734197db83d026f1895970e3ce71ab770b5f17fd95717b6270c6e69fb2b99

Observation 28b4a498-04c2-4693-9ec1-9ab0f459b858 · outbound

This paper cites , Dayan , P.

Quantum reinforcement learning in dynamic environments , Dayan , P

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-06T20:53:53.358936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:53:53.358936Z digest=sha256:2c51380c6968d50b621bc81cdb9f916d8e685653838bb3991ef5a68a2cece5bc

Observation 16debd30-cc57-4326-94f8-eea268b67d4f · outbound

This paper cites Quantum Policy Iteration via Amplitude Estimation and Grover Search -- Towards Quantum Advantage for Reinforcement Learning.

Quantum reinforcement learning in dynamic environments Quantum Policy Iteration via Amplitude Estimation and Grover Search -- Towards Quantum Advantage for Reinforcement Learning

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-06T20:53:53.445777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:53:53.445777Z digest=sha256:ca32f62d3ec66e57aade843e82ca0303f4c8ecf09b9eb69c6c04476f5face206

Observation 2cd75ace-30f7-4ae8-9e87-97579b73dc62 · outbound

This paper cites : Simple statistical gradient-following algorithms for connectionist reinforcement learning.

Quantum reinforcement learning in dynamic environments : Simple statistical gradient-following algorithms for connectionist reinforcement learning

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-06T20:53:53.517354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:53:53.517354Z digest=sha256:9c7f2c0fa400d7a2d85fd8444e7077eceffad6d9b10bc12d78d120e6594b2dfc

Observation d65a9081-75b0-42aa-95ec-22759b26015a · outbound

This paper cites , Jin , S.

Quantum reinforcement learning in dynamic environments , Jin , S

Reference 58

Resolution
verified exact
doi, observed 2026-08-06T20:53:54.070042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-06T20:53:53.616842Z digest=sha256:6d536f940886a3985efe233ab5bd73964784361f649497bb9ef9891eff63842f

Observation ababe30b-1db1-4bb3-947b-19622a51b47c · outbound

This paper cites Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret.

Quantum reinforcement learning in dynamic environments Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret

Reference 59

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unresolved
no resolver link, observed 2026-08-06T20:53:53.706900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:53:53.706900Z digest=sha256:88373cb0ee99809401f682200ef59b9e5ff28511474fa3af9bc1aa470a4e646e

Observation b4038d34-0650-4519-9b81-f71ca1e88fda · outbound

This paper cites Fine-Tuning Language Models from Human Preferences.

Quantum reinforcement learning in dynamic environments Fine-Tuning Language Models from Human Preferences

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-06T20:53:53.781669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:53:53.781669Z digest=sha256:2f3171f155debb21e06fc3b46455c2fc9c13aa5d0c458a16eb5e394fbdd30e71

Observation b7a1847b-9d24-45d1-9dab-745360ae442b · outbound

This paper cites write newline.

Quantum reinforcement learning in dynamic environments write newline

Reference 61

Resolution
malformed identifier
no resolver link, observed 2026-08-06T20:53:53.890488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:53:53.890488Z digest=sha256:6e8b99d8ef8fc226ee4dfe96ec8a5694aa201b0f21836e72cea5170770cb1977

Pith citing papers

Observation 59b9ada6-54ca-4ecf-b770-98fa0d460cfe · inbound

Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation cites this paper.

Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation Quantum reinforcement learning in dynamic environments

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-05-21T04:53:58.088564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T04:50:37.976670Z digest=sha256:4491b6d5d64d2e73f65f042972f1d5f4cf4cda9d8a07ab034e2d9e9bdb7d32e0

Observation fc056375-29f7-4295-ad73-149d1e28b357 · inbound

Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation cites this paper.

Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation Quantum reinforcement learning in dynamic environments

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-02T13:36:11.767803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T13:36:11.767803Z digest=sha256:cdd061572e0f75d9bc517f383cb2a77796166a87894bf38a9c43e0b0caeceae6