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Paper Citation Record · LEDGER

Optimizing Tensor Network Partitioning using Simulated Annealing

As of 17 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2507.20667.

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

pith.paper-citation-record.v1
2507.20667 v1

Coverage vector

measured 41 of 41 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-06T13:27:38.479235Z

measured 41 of 41 standing notices

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

measured 0 of 0 inbound itemization

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Reference resolution

41 of 41 outbound references displayed

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External citation measurements

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Outbound references

Observation 052dbeb6-1d9b-4b4e-9042-8aa7b8b54b70 · outbound

This paper cites Andre, S.

Optimizing Tensor Network Partitioning using Simulated Annealing Andre, S

Reference 1

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Observation c6e8a410-9b03-4891-b0ce-e535b195eecc · outbound

This paper cites Arute, K.

Optimizing Tensor Network Partitioning using Simulated Annealing Arute, K

Reference 2

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Observation 25a7481d-a47f-45d0-81d5-3ef995eabe7d · outbound

This paper cites Brennan, M.

Optimizing Tensor Network Partitioning using Simulated Annealing Brennan, M

Reference 3

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Observation 4bebd045-2806-45da-9fc0-3590e3615141 · outbound

This paper cites ˇCern´y, Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm , Journal of Optimization Theory and Applications, 45 (1985), pp.

Optimizing Tensor Network Partitioning using Simulated Annealing ˇCern´y, Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm , Journal of Optimization Theory and Applications, 45 (1985), pp

Reference 4

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Observation 0fd93c46-5e1f-4dc0-af30-98fdce0a3083 · outbound

This paper cites Classical Simulation of Intermediate-Size Quantum Circuits.

Optimizing Tensor Network Partitioning using Simulated Annealing Classical Simulation of Intermediate-Size Quantum Circuits

Reference 5

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Observation ec371da3-708a-4211-92a7-8bba8da16487 · outbound

This paper cites Chi-Chung, P.

Optimizing Tensor Network Partitioning using Simulated Annealing Chi-Chung, P

Reference 6

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Observation d1b2ee41-c996-4644-bf74-142d31e54955 · outbound

This paper cites Survey on Computational Applications of Tensor Network Simulations.

Optimizing Tensor Network Partitioning using Simulated Annealing Survey on Computational Applications of Tensor Network Simulations

Reference 7

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Observation 0cedf087-c48a-47ef-9b91-40ba3030f4b7 · outbound

This paper cites Gray, cotengra.

Optimizing Tensor Network Partitioning using Simulated Annealing Gray, cotengra

Reference 8

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Observation 14e9c026-66fb-4114-a8f1-60a705610c4f · outbound

This paper cites Gray and G.

Optimizing Tensor Network Partitioning using Simulated Annealing Gray and G

Reference 9

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Observation 96abc7c8-385c-42c9-a7f9-a21951dd0e56 · outbound

This paper cites Gray and S.

Optimizing Tensor Network Partitioning using Simulated Annealing Gray and S

Reference 10

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Observation 8dcd961a-a99c-4c21-929e-4377e3e34d0b · outbound

This paper cites Hamann and B.

Optimizing Tensor Network Partitioning using Simulated Annealing Hamann and B

Reference 11

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Observation d4cb0ecd-dc06-434f-a4ce-85857476ac3d · outbound

This paper cites Classical Simulation of Quantum Supremacy Circuits.

Optimizing Tensor Network Partitioning using Simulated Annealing Classical Simulation of Quantum Supremacy Circuits

Reference 12

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Observation 29ed803e-1ecc-480f-84c3-86245cdf47a5 · outbound

This paper cites Huang, F.

Optimizing Tensor Network Partitioning using Simulated Annealing Huang, F

Reference 13

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Observation c25c75af-1b7d-4558-8a5c-620cce155bb3 · outbound

This paper cites Ibrahim, D.

Optimizing Tensor Network Partitioning using Simulated Annealing Ibrahim, D

Reference 14

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This paper cites https://software.intel.com/en-us/intel-mkl.

Optimizing Tensor Network Partitioning using Simulated Annealing https://software.intel.com/en-us/intel-mkl

Reference 15

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Observation b96ad2f8-6924-47bb-a8f4-8e6f55c1b283 · outbound

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Optimizing Tensor Network Partitioning using Simulated Annealing Unresolved cited work

Reference 16

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Observation 4a7b5d94-c56f-42fe-9db1-9b39dd502035 · outbound

This paper cites Kirkpatrick, C.

Optimizing Tensor Network Partitioning using Simulated Annealing Kirkpatrick, C

Reference 17

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Observation d78ba36e-c0c6-4a99-b9d3-aa7e657fcfcc · outbound

This paper cites Kjolstad, S.

Optimizing Tensor Network Partitioning using Simulated Annealing Kjolstad, S

Reference 18

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Observation 9281edd1-e3d8-4c83-872f-ca7f2b2aa386 · outbound

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Optimizing Tensor Network Partitioning using Simulated Annealing Unresolved cited work

Reference 19

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Observation 5672c118-0b8d-47d6-b0b5-bd65f59b2617 · outbound

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Optimizing Tensor Network Partitioning using Simulated Annealing Unresolved cited work

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Observation af2b6629-50ea-40ce-9d71-a0732d09f274 · outbound

This paper cites Mascagni, E.

Optimizing Tensor Network Partitioning using Simulated Annealing Mascagni, E

Reference 21

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Observation 930911b2-6b37-40cb-9094-3793abc3925c · outbound

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Optimizing Tensor Network Partitioning using Simulated Annealing Unresolved cited work

Reference 22

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Observation b937e849-4858-4616-90ac-a7135a68b292 · outbound

This paper cites Massively Parallel Tensor Network State Algorithms on Hybrid CPU-GPU Based Architectures.

Optimizing Tensor Network Partitioning using Simulated Annealing Massively Parallel Tensor Network State Algorithms on Hybrid CPU-GPU Based Architectures

Reference 23

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Observation 6bcb9122-e5e0-4ed7-9318-c82b59b6ebb4 · outbound

This paper cites https://docs.nvidia.com/cuda/cuquantum/latest/cutensornet/index.html, 2024.

Optimizing Tensor Network Partitioning using Simulated Annealing https://docs.nvidia.com/cuda/cuquantum/latest/cutensornet/index.html, 2024

Reference 24

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Observation bf6d567f-6a0b-4992-af32-289e616728b1 · outbound

This paper cites https://developer.nvidia.com/cutensor, 2025.

Optimizing Tensor Network Partitioning using Simulated Annealing https://developer.nvidia.com/cutensor, 2025

Reference 25

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Observation bef68fce-5454-40f2-b982-66223dd677a0 · outbound

This paper cites O’Gorman, Parameterization of Tensor Network Contraction , in 14th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2019), W.

Optimizing Tensor Network Partitioning using Simulated Annealing O’Gorman, Parameterization of Tensor Network Contraction , in 14th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2019), W

Reference 26

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Observation b06fa2e8-8ab2-4cc7-9fad-7fea8a4354ff · outbound

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

Optimizing Tensor Network Partitioning using Simulated Annealing Or´us, Tensor networks for complex quantum systems , Nature Reviews Physics, 1 (2019), p

Reference 27

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Observation 16d21893-32bd-4228-bca3-6f949542981f · outbound

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Optimizing Tensor Network Partitioning using Simulated Annealing Unresolved cited work

Reference 28

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This paper cites Simulating the Sycamore quantum supremacy circuits.

Optimizing Tensor Network Partitioning using Simulated Annealing Simulating the Sycamore quantum supremacy circuits

Reference 29

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Observation a41cced9-75d8-4abe-a0b8-0ba5f36aa3d9 · outbound

This paper cites Patra, S.

Optimizing Tensor Network Partitioning using Simulated Annealing Patra, S

Reference 30

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Observation 9686f975-026f-494d-bf29-dba36f710170 · outbound

This paper cites Pareto-Efficient Quantum Circuit Simulation Using Tensor Contraction Deferral.

Optimizing Tensor Network Partitioning using Simulated Annealing Pareto-Efficient Quantum Circuit Simulation Using Tensor Contraction Deferral

Reference 31

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Observation 039ee91f-208a-4090-b5f5-469bbdd78426 · outbound

This paper cites Matrix Product State Representations.

Optimizing Tensor Network Partitioning using Simulated Annealing Matrix Product State Representations

Reference 32

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Observation 53641ca3-7432-478f-8720-f75fb1152099 · outbound

This paper cites Quetschlich, L.

Optimizing Tensor Network Partitioning using Simulated Annealing Quetschlich, L

Reference 33

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Observation ec827ce6-aff9-4c11-9228-d9bde829565e · outbound

This paper cites The computational difficulty of finding MPS ground states.

Optimizing Tensor Network Partitioning using Simulated Annealing The computational difficulty of finding MPS ground states

Reference 34

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Observation e0a8be5a-e613-4f71-9d78-385668cf55ab · outbound

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Optimizing Tensor Network Partitioning using Simulated Annealing Unresolved cited work

Reference 35

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Observation 3d050082-de66-417f-91a0-006e353071a5 · outbound

This paper cites Springer, T.

Optimizing Tensor Network Partitioning using Simulated Annealing Springer, T

Reference 36

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Observation fa99e77e-dfaa-4a70-a5de-3381287d931b · outbound

This paper cites Stoian, An efficient implementation of polynomial-time join ordering , bachelor’s thesis, Technical University of Munich, Germany, 2021.

Optimizing Tensor Network Partitioning using Simulated Annealing Stoian, An efficient implementation of polynomial-time join ordering , bachelor’s thesis, Technical University of Munich, Germany, 2021

Reference 37

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Observation 2997d19e-7e78-4956-a3ed-30aeaff38fc7 · outbound

This paper cites V an Damme, R.

Optimizing Tensor Network Partitioning using Simulated Annealing V an Damme, R

Reference 38

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

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Observation 80bb3d63-5fc2-4b3b-b9bb-63ad156f50b3 · outbound

This paper cites Matrix Product States, Projected Entangled Pair States, and variational renormalization group methods for quantum spin systems.

Optimizing Tensor Network Partitioning using Simulated Annealing Matrix Product States, Projected Entangled Pair States, and variational renormalization group methods for quantum spin systems

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-06T13:27:38.469258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:27:38.469258Z digest=sha256:2a722836f312dfd7e41fca8e9431aee84c6c27b09b0b2d335056e04d4898aab0

Observation 424b07ef-5beb-4d4b-b49f-b5aed4e92578 · outbound

This paper cites Vincent, L.

Optimizing Tensor Network Partitioning using Simulated Annealing Vincent, L

Reference 40

Resolution
verified exact
doi, observed 2026-08-06T13:27:38.539634Z

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=2026-08-06T13:27:38.474213Z digest=sha256:18ecfb96a64053741da47da269c9b147cc4f5041778ec7058fc50aa4710795b6

Observation bb55d824-200e-4e2d-9c19-14c8f73c6be8 · outbound

This paper cites an unresolved cited work.

Optimizing Tensor Network Partitioning using Simulated Annealing Unresolved cited work

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-06T13:27:38.479235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:27:38.479235Z digest=sha256:6a3249a63a0e8164e19619613cfafda16c422456cb5358b55ff6bd736baa40e0

Pith citing papers

No inbound Pith citation observations are available.