Paper Citation Record · LEDGER

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines

As of 21 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 1 inbound Pith citation observation for arXiv:2411.16431.

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

pith.paper-citation-record.v1
2411.16431 v3

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:13:17.783306Z

measured 66 of 66 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-23T04:38:56.945516Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T04:42:34.351406Z

Reference resolution

65 of 65 outbound references displayed

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External citation measurements

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Outbound references

Observation 75fb528b-a0d3-425e-b7ee-84d7d6c12b83 · outbound

This paper cites Specifically, for asitetensor the bond dimensionsD l, Du, Dr,D d, see 7 χ Dl χ χ Dr χ Du Dd = d χ Dr χ χ Dl χ Dx′ Dx′′ = dnmdmn dmn′ dnm′ dmn′′ dnm′′ (a) (b) (c) (d) FIG.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Specifically, for asitetensor the bond dimensionsD l, Du, Dr,D d, see 7 χ Dl χ χ Dr χ Du Dd = d χ Dr χ χ Dl χ Dx′ Dx′′ = dnmdmn dmn′ dnm′ dmn′′ dnm′′ (a) (b) (c) (d) FIG

Reference 1

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Observation 4506be36-4bef-40c5-aba0-eb38201fbc14 · outbound

This paper cites A particular challenge here is the bond dimensions of MPO tensors (siteandmediating tensors); see the previous section.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines A particular challenge here is the bond dimensions of MPO tensors (siteandmediating tensors); see the previous section

Reference 2

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Observation b29fb1e6-9233-4c0c-bfed-8e7bbbc01547 · outbound

This paper cites Barahona, On the computational complexity of Ising spin glass models, J.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Barahona, On the computational complexity of Ising spin glass models, J

Reference 3

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Observation 0c1353d7-137c-4a35-964a-b5146727d8de · outbound

This paper cites Lucas, Ising formulations of many NP problems, Front.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Lucas, Ising formulations of many NP problems, Front

Reference 4

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Observation fe284f58-0406-47fb-82c2-8ecc1f57bee8 · outbound

This paper cites De las Cuevas and T.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines De las Cuevas and T

Reference 5

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Observation 87b10d71-2888-44d9-8e61-8c1fea43ae46 · outbound

This paper cites Kirkpatrick, C.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Kirkpatrick, C

Reference 6

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Observation a2758d36-a2f9-4f6e-950e-26a75dd7b73c · outbound

This paper cites Metropolis and S.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Metropolis and S

Reference 7

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Unresolved cited work

Reference 8

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Unresolved cited work

Reference 9

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Observation d2285f72-098b-4f3b-886d-32d52e6f3422 · outbound

This paper cites Houdayer, A cluster Monte Carlo algorithm for 2- dimensional spin glasses, Eur.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Houdayer, A cluster Monte Carlo algorithm for 2- dimensional spin glasses, Eur

Reference 10

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Unresolved cited work

Reference 11

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This paper cites Nonequilibrium Monte Carlo for unfreezing variables in hard combinatorial optimization.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Nonequilibrium Monte Carlo for unfreezing variables in hard combinatorial optimization

Reference 12

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This paper cites Efficient subgraph-based sampling of Ising-type models with frustration.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Efficient subgraph-based sampling of Ising-type models with frustration

Reference 13

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This paper cites Isakov, I.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Isakov, I

Reference 14

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Observation 383c797a-0beb-4f2d-9fcb-52db1fed91b5 · outbound

This paper cites Kadowaki and H.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Kadowaki and H

Reference 15

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Unresolved cited work

Reference 16

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Unresolved cited work

Reference 17

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This paper cites Bernaschi, I.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Bernaschi, I

Reference 18

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This paper cites Munoz-Bauza and D.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Munoz-Bauza and D

Reference 19

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Unresolved cited work

Reference 20

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Unresolved cited work

Reference 21

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This paper cites Bando, Y.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Bando, Y

Reference 22

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Observation dd28e128-bfcb-4ce7-976f-9ce4ba7125f1 · outbound

This paper cites Kashimata, M.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Kashimata, M

Reference 23

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Unresolved cited work

Reference 24

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Unresolved cited work

Reference 25

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Verstraete, V

Reference 26

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This paper cites Or´ us, A practical introduction to tensor networks: Matrix product states and projected entangled pair states, Ann.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Or´ us, A practical introduction to tensor networks: Matrix product states and projected entangled pair states, Ann

Reference 27

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This paper cites Tensor Networks in a Nutshell.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Tensor Networks in a Nutshell

Reference 28

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Unresolved cited work

Reference 29

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Unresolved cited work

Reference 30

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Nishino, Y

Reference 31

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Schuch, M

Reference 32

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Unresolved cited work

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Unresolved cited work

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Frias-Perez, M

Reference 35

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Unresolved cited work

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Gray and G

Reference 37

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Unresolved cited work

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Mezard and A

Reference 39

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Pegasus: The second connectivity graph for large-scale quantum annealing hardware

Reference 40

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Next-Generation Topology of D-Wave Quantum Processors

Reference 41

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Boothby, A

Reference 42

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Verstraete, M

Reference 43

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Lubasch, J

Reference 44

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Halko, P.-G

Reference 45

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Martinsson and J

Reference 46

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Unresolved cited work

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Sinha, M

Reference 48

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Unresolved cited work

Reference 49

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This paper cites Pearl, Reverend Bayes on inference engines: A distributed hierarchical approach, inProbabilistic and Causal Inference(Association for Computing Machinery, 1982).

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Pearl, Reverend Bayes on inference engines: A distributed hierarchical approach, inProbabilistic and Causal Inference(Association for Computing Machinery, 1982)

Reference 50

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Yedidia, W

Reference 51

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Unresolved cited work

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Tasseff, T

Reference 53

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Perera, F

Reference 54

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Chook -- A comprehensive suite for generating binary optimization problems with planted solutions

Reference 55

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This paper cites Accuracy and Performance Evaluation of Quantum, Classical and Hybrid Solvers for the Max-Cut Problem.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Accuracy and Performance Evaluation of Quantum, Classical and Hybrid Solvers for the Max-Cut Problem

Reference 56

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Observation aa05537d-03bc-4920-bcbd-03f2bb2bb3d2 · outbound

This paper cites Mohseni, M.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Mohseni, M

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:13:18.066586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2548d6ba-8b19-437d-bae8-b6a12fdd8e5d · outbound

This paper cites Diversity metric for evaluation of quantum annealing.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Diversity metric for evaluation of quantum annealing

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-12T13:13:17.751955Z

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Observation f8a1aac3-9dd7-4767-8263-05f7ef73a3aa · outbound

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Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:13:18.052764Z

Source-reported events for the cited work

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Observation 5cb7ccca-b1b8-4d1f-8a1b-27609d974c51 · outbound

This paper cites At each step of the iteration, a configuration is added to the list of independent states if it is independent of all configura- tions already in the list.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines At each step of the iteration, a configuration is added to the list of independent states if it is independent of all configura- tions already in the list

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:13:18.039550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7de7ea00-665f-44a3-8b3c-7a83af6c5dc5 · outbound

This paper cites an unresolved cited work.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Unresolved cited work

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-12T13:13:17.765263Z

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Observation bf78b272-feef-4bd9-a438-ef8cd4252099 · outbound

This paper cites How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-12T13:13:17.769120Z

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Observation 065d70c9-748a-405f-90b3-d45a48fd8bdd · outbound

This paper cites Pedretti, F.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Pedretti, F

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:13:18.016705Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1838ea3c-fd75-435a-ad1f-03989bce95f0 · outbound

This paper cites Chowdhury, A.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines Chowdhury, A

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:13:18.003669Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1b3c593f-08cc-4385-b26d-8829cea5bb5d · outbound

This paper cites ´Smiechrzalski, A.

Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines ´Smiechrzalski, A

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-12T13:13:17.783306Z

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Pith citing papers

Observation 81615225-7f07-409a-8374-a6fe43dc4e54 · inbound

VeloxQ: A Fast and Efficient QUBO Solver cites this paper.

VeloxQ: A Fast and Efficient QUBO Solver Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines

Reference 47

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.