Paper Citation Record · LEDGER

Learning Circuits with Infinite Tensor Networks

As of 17 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 5 inbound Pith citation observations for arXiv:2506.02105.

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

pith.paper-citation-record.v1
2506.02105 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:36:39.012839Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:47:26.615440Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

42 of 42 outbound references displayed

verified exact3
verified fuzzy10
unresolved29
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 792094bf-de70-473d-b3a2-24d9caed3193 · outbound

This paper cites (7) Throughout our analysis we fix J = 1.0.

Learning Circuits with Infinite Tensor Networks (7) Throughout our analysis we fix J = 1.0

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:36:40.316345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 97efd3be-5b5e-495f-a86b-82e88cb7757d · outbound

This paper cites an unresolved cited work.

Learning Circuits with Infinite Tensor Networks Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:36:40.298509Z

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Observation 4e2e09c2-fe61-4666-b2fa-dd4368874d13 · outbound

This paper cites odd” and “even.

Learning Circuits with Infinite Tensor Networks odd” and “even

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:36:40.279062Z

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Observation 3a0fc32c-02de-479d-b4a6-864d28070494 · outbound

This paper cites an unresolved cited work.

Learning Circuits with Infinite Tensor Networks Unresolved cited work

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:38.799562Z

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Observation eb7d4d3d-b297-44d9-9e66-5c5ce70d3b90 · outbound

This paper cites Preskill, Quantum computing in the NISQ era and beyond, Quantum 2, 79 (2018).

Learning Circuits with Infinite Tensor Networks Preskill, Quantum computing in the NISQ era and beyond, Quantum 2, 79 (2018)

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:38.804744Z

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Observation 01344914-70ca-46bf-a37b-3c440714aca2 · outbound

This paper cites Bravyi and J.

Learning Circuits with Infinite Tensor Networks Bravyi and J

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:36:40.223486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f6e2e190-55a1-43f0-b117-a6a7f62930f3 · outbound

This paper cites an unresolved cited work.

Learning Circuits with Infinite Tensor Networks Unresolved cited work

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:38.815543Z

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Observation 5928b3d8-40e1-4188-abbc-c4ba5fd99a75 · outbound

This paper cites Faster quantum chemistry simulations on a quantum computer with improved tensor factorization and active volume compilation.

Learning Circuits with Infinite Tensor Networks Faster quantum chemistry simulations on a quantum computer with improved tensor factorization and active volume compilation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:38.820366Z

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Observation a4ea25b8-7cef-42aa-bf5e-bc59ae271241 · outbound

This paper cites Quantum circuit optimization with deep reinforcement learning.

Learning Circuits with Infinite Tensor Networks Quantum circuit optimization with deep reinforcement learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:38.830906Z

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Observation 1f9ae97c-61f5-4aa2-b528-593389340cdf · outbound

This paper cites an unresolved cited work.

Learning Circuits with Infinite Tensor Networks Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:36:40.192529Z

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Observation d57690bb-7261-4a9a-b49a-4f5fb378b3d7 · outbound

This paper cites Duncan, A.

Learning Circuits with Infinite Tensor Networks Duncan, A

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:36:40.169221Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 860ee35e-58be-45c7-99f8-14f47d96795c · outbound

This paper cites Or´ us, Tensor networks for complex quantum systems, Nature Reviews Physics 1, 538 (2019).

Learning Circuits with Infinite Tensor Networks Or´ us, Tensor networks for complex quantum systems, Nature Reviews Physics 1, 538 (2019)

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:38.848764Z

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Observation 0af1fba1-dd2b-4fca-9333-9ca4ada4c096 · outbound

This paper cites Vidal, Efficient classical simulation of slightly entan- gled quantum computations, Phys.

Learning Circuits with Infinite Tensor Networks Vidal, Efficient classical simulation of slightly entan- gled quantum computations, Phys

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:38.854353Z

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Observation 70edd9fb-0062-4293-af4a-a9f76165de13 · outbound

This paper cites Berezutskii, A.

Learning Circuits with Infinite Tensor Networks Berezutskii, A

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:38.860113Z

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Observation 07146b59-893f-45ce-be02-e69b74157df3 · outbound

This paper cites an unresolved cited work.

Learning Circuits with Infinite Tensor Networks Unresolved cited work

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:38.865443Z

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Observation 6bcbb951-7ab9-4f2b-9df0-af8ec4758c56 · outbound

This paper cites an unresolved cited work.

Learning Circuits with Infinite Tensor Networks Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:36:40.112142Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 26e8db90-1cbd-4155-8bbd-0dc0f28493a5 · outbound

This paper cites Dborin, F.

Learning Circuits with Infinite Tensor Networks Dborin, F

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:38.878166Z

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Observation 95854fa5-1249-4e41-ac64-00e096de7394 · outbound

This paper cites Jamet, C.

Learning Circuits with Infinite Tensor Networks Jamet, C

Reference 18

Resolution: verified exact; raw_fallback, observed 2026-08-07T11:36:39.629772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9cc90e76-93e3-487c-b96d-baccc8e6252e · outbound

This paper cites Quantum Circuit Optimization using Differentiable Programming of Tensor Network States.

Learning Circuits with Infinite Tensor Networks Quantum Circuit Optimization using Differentiable Programming of Tensor Network States

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:38.888634Z

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Observation 0bd02bfd-9bce-457d-bb11-766a974bce73 · outbound

This paper cites an unresolved cited work.

Learning Circuits with Infinite Tensor Networks Unresolved cited work

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:38.894434Z

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Observation 472db348-69ae-465f-9123-26b233aaa067 · outbound

This paper cites Anselme Martin, T.

Learning Circuits with Infinite Tensor Networks Anselme Martin, T

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:38.899237Z

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Observation f58d1f72-0f84-4ac6-aa2d-360ebc64b272 · outbound

This paper cites Causer, F.

Learning Circuits with Infinite Tensor Networks Causer, F

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:38.904678Z

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Observation 87121cb3-73d1-44d2-9408-6711a55f4533 · outbound

This paper cites Mc Keever and M.

Learning Circuits with Infinite Tensor Networks Mc Keever and M

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:36:40.021517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8e941570-b576-45c1-8b8b-16130fc5c67a · outbound

This paper cites Deep Circuit Compression for Quantum Dynamics via Tensor Networks.

Learning Circuits with Infinite Tensor Networks Deep Circuit Compression for Quantum Dynamics via Tensor Networks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:38.914722Z

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Observation ccb38241-30c8-41a6-bb84-396fdace13bc · outbound

This paper cites Scalable quantum dynamics compilation via quantum machine learning.

Learning Circuits with Infinite Tensor Networks Scalable quantum dynamics compilation via quantum machine learning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:38.919867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3c7f048-4836-4740-9d7b-e799bfd0aeeb · outbound

This paper cites an unresolved cited work.

Learning Circuits with Infinite Tensor Networks Unresolved cited work

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:38.924469Z

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Observation 69fadb95-40d0-4d66-ae08-e69b2b9d07f0 · outbound

This paper cites Mansuroglu, T.

Learning Circuits with Infinite Tensor Networks Mansuroglu, T

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:36:40.002908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6c27655b-d032-4716-8aa9-94335ee4ef77 · outbound

This paper cites Fully optimised variational simulation of a dynamical quantum phase transition on a trapped-ion quantum computer.

Learning Circuits with Infinite Tensor Networks Fully optimised variational simulation of a dynamical quantum phase transition on a trapped-ion quantum computer

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:38.934785Z

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Observation 4b01a8ed-1f64-4804-833a-6bcb174bbfb2 · outbound

This paper cites Vidal, Classical simulation of infinite-size quantum lattice systems in one spatial dimension, Physical Review Letters 98, 070201 (2007).

Learning Circuits with Infinite Tensor Networks Vidal, Classical simulation of infinite-size quantum lattice systems in one spatial dimension, Physical Review Letters 98, 070201 (2007)

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:36:39.985706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation df99cda4-c7ac-4969-a656-aa6e9e0c47ad · outbound

This paper cites Zauner-Stauber, L.

Learning Circuits with Infinite Tensor Networks Zauner-Stauber, L

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:36:39.967876Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 32eb8ebf-a433-401f-9fde-74ad66d57a94 · outbound

This paper cites an unresolved cited work.

Learning Circuits with Infinite Tensor Networks Unresolved cited work

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:38.952667Z

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Observation ac4cf2ee-98e8-4494-9a4a-943b623d1675 · outbound

This paper cites Bang-bang preparation of a quantum many-body ground state in a finite lattice: optimization of the algorithm with a tensor network.

Learning Circuits with Infinite Tensor Networks Bang-bang preparation of a quantum many-body ground state in a finite lattice: optimization of the algorithm with a tensor network

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:36:39.184384Z

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Observation 7ba77e1f-1ee9-4796-944e-d7a9f8cefcab · outbound

This paper cites an unresolved cited work.

Learning Circuits with Infinite Tensor Networks Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:36:39.757178Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 238d3a0e-cb85-49e3-bd85-27b2fd240d99 · outbound

This paper cites Mogensen and A.

Learning Circuits with Infinite Tensor Networks Mogensen and A

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:38.971358Z

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Observation c455aa17-b530-48b8-a4cb-e0b7ebff53b3 · outbound

This paper cites an unresolved cited work.

Learning Circuits with Infinite Tensor Networks Unresolved cited work

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:38.976744Z

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Observation 62160ced-9e5c-46c9-948e-90fcbf9cf51a · outbound

This paper cites Kogut and L.

Learning Circuits with Infinite Tensor Networks Kogut and L

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:36:39.727739Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 11f36358-a32d-4114-8323-0b6fac28a949 · outbound

This paper cites Pfeuty, The one-dimensional Ising model with a trans- verse field, ANNALS of Physics 57, 79 (1970).

Learning Circuits with Infinite Tensor Networks Pfeuty, The one-dimensional Ising model with a trans- verse field, ANNALS of Physics 57, 79 (1970)

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:36:39.710926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4b2ef349-fdbe-4d5e-ba3e-11b325a41c95 · outbound

This paper cites Schollw¨ ock, The density-matrix renormalization group in the age of matrix product states, Annals of physics 326, 96 (2011).

Learning Circuits with Infinite Tensor Networks Schollw¨ ock, The density-matrix renormalization group in the age of matrix product states, Annals of physics 326, 96 (2011)

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:38.991925Z

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Observation fbe39c19-c38b-406a-af13-8947652e91d2 · outbound

This paper cites An Introduction to Cartan's KAK Decomposition for QC Programmers.

Learning Circuits with Infinite Tensor Networks An Introduction to Cartan's KAK Decomposition for QC Programmers

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:38.997834Z

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Observation d22182b1-e683-4437-afee-6da21af1822d · outbound

This paper cites an unresolved cited work.

Learning Circuits with Infinite Tensor Networks Unresolved cited work

Reference 40

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:36:39.668174Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 17b14431-aea4-4503-9bca-3886620b00df · outbound

This paper cites an unresolved cited work.

Learning Circuits with Infinite Tensor Networks Unresolved cited work

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T11:36:39.008229Z

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Observation 5c7c4031-ac83-41be-9a2f-b16900b01c37 · outbound

This paper cites Learning Circuits with Infinite Tensor Networks.

Learning Circuits with Infinite Tensor Networks Learning Circuits with Infinite Tensor Networks

Reference 42

Resolution: verified exact; doi, observed 2026-08-07T11:36:39.132959Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation 49fe4051-457b-438e-8181-10b24c30c1b9 · inbound

Resource-Efficient Simulations of Particle Scattering on a Digital Quantum Computer cites this paper.

Resource-Efficient Simulations of Particle Scattering on a Digital Quantum Computer Learning Circuits with Infinite Tensor Networks

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T14:47:26.615440Z

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Observation d1654b1d-88d3-4f44-8448-1405360f5ac9 · inbound

Numerical Experiments with Parameter Setting of Trotterized Quantum Phase Estimation for Quantum Hamiltonian Ground State Computation cites this paper.

Numerical Experiments with Parameter Setting of Trotterized Quantum Phase Estimation for Quantum Hamiltonian Ground State Computation Learning Circuits with Infinite Tensor Networks

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-02T20:48:39.424885Z

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Observation c396879f-208e-498e-9b80-6330d9f469ab · inbound

Simulating the dynamics of an SU(2) matrix model on a trapped-ion quantum computer cites this paper.

Simulating the dynamics of an SU(2) matrix model on a trapped-ion quantum computer Learning Circuits with Infinite Tensor Networks

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-10T13:45:27.009861Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a839560c-63e0-42b6-a328-f574d851a530 · inbound

Hessian-vector products for tensor networks via recursive tangent-state propagation cites this paper.

Hessian-vector products for tensor networks via recursive tangent-state propagation Learning Circuits with Infinite Tensor Networks

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-10T00:29:47.538772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 88b625c9-de6b-408c-8412-9a8f3abe1626 · inbound

Exponentially many initializations to avoid barren plateaus cites this paper.

Exponentially many initializations to avoid barren plateaus Learning Circuits with Infinite Tensor Networks

Reference 94

Resolution: verified exact; arxiv_id, observed 2026-06-27T00:00:15.249882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.