Paper Citation Record · LEDGER

Implicit differentiation of tensor network algorithms

As of 10 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 1 inbound Pith citation observation for arXiv:2607.15030.

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

pith.paper-citation-record.v1
2607.15030 v1

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T00:28:22.998056Z

measured 63 of 63 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T00:10:29.252808Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

62 of 62 outbound references displayed

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unresolved62
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External citation measurements

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Outbound references

Observation fbfbc318-fde7-4b14-9f3c-3207ce96fd68 · outbound

This paper cites A real-valued cost functioneof the complex parametersp is given by a mape:C n →R.

Implicit differentiation of tensor network algorithms A real-valued cost functioneof the complex parametersp is given by a mape:C n →R

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T00:28:16.617735Z

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Observation 741b3ee2-7a89-40c7-b260-fe2fcf1e9f6a · outbound

This paper cites an unresolved cited work.

Implicit differentiation of tensor network algorithms Unresolved cited work

Reference 2

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Observation cdfeb84a-cfb3-4c4b-8397-8dbb4be52c70 · outbound

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Implicit differentiation of tensor network algorithms Unresolved cited work

Reference 3

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Observation 6b2cf371-e6ea-43c0-9840-4eca83ae0a4f · outbound

This paper cites 22 Points 1.

Implicit differentiation of tensor network algorithms 22 Points 1

Reference 4

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Observation 9f6c0284-a6b0-4719-9ddf-2b9c0f91e0f4 · outbound

This paper cites Having introduced the notions of JVP, VJP and reverse-mode accumulation, we can take a step back and phrase things a bit more informally.

Implicit differentiation of tensor network algorithms Having introduced the notions of JVP, VJP and reverse-mode accumulation, we can take a step back and phrase things a bit more informally

Reference 5

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Observation 3777efc3-0554-42eb-91ee-35096a1a79c1 · outbound

This paper cites symmetric.

Implicit differentiation of tensor network algorithms symmetric

Reference 6

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Observation f5d83ad2-6ee2-4f4a-bb67-a5a669cbf6d0 · outbound

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Implicit differentiation of tensor network algorithms Unresolved cited work

Reference 7

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Observation 4f9a94f2-c1de-42dd-89d6-d85ecece9162 · outbound

This paper cites an unresolved cited work.

Implicit differentiation of tensor network algorithms Unresolved cited work

Reference 8

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Observation 3ce07397-c14a-4979-92e4-9e20567d8963 · outbound

This paper cites Crucially, all of these forward schemes converge to the same fixed point, meaning they can use the same char- acteristic equation in the backwards pass.

Implicit differentiation of tensor network algorithms Crucially, all of these forward schemes converge to the same fixed point, meaning they can use the same char- acteristic equation in the backwards pass

Reference 9

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Observation 3030c98f-24e9-45b5-bb36-14272a5b138a · outbound

This paper cites Or´ us, Tensor networks for complex quantum systems, Nat.

Implicit differentiation of tensor network algorithms Or´ us, Tensor networks for complex quantum systems, Nat

Reference 10

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Observation 4c5b709e-acec-45eb-ae8b-49228f51b66e · outbound

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Implicit differentiation of tensor network algorithms Unresolved cited work

Reference 11

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Observation 10dd9ac2-0630-4ec5-a499-a424a6195ee7 · outbound

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Implicit differentiation of tensor network algorithms Unresolved cited work

Reference 12

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Observation 5512903e-243c-4c78-a404-b386aca66cfd · outbound

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Implicit differentiation of tensor network algorithms Unresolved cited work

Reference 13

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Observation 2baded24-8af0-42c5-ade2-df05b83e38b3 · outbound

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Implicit differentiation of tensor network algorithms Unresolved cited work

Reference 14

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Observation f3ac0a1f-59e3-4ae7-a2d2-1f31ad9e20a7 · outbound

This paper cites Verstraete, D.

Implicit differentiation of tensor network algorithms Verstraete, D

Reference 15

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Observation 8ea23d8e-4665-419b-95e8-44c47eec2f9b · outbound

This paper cites Schollw¨ ock, The density-matrix renormalization group in the age of matrix product states, Ann.

Implicit differentiation of tensor network algorithms Schollw¨ ock, The density-matrix renormalization group in the age of matrix product states, Ann

Reference 16

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Observation b94dbd5e-d482-4e99-97f4-5ecb7d52f498 · outbound

This paper cites ¨Ostlund and S.

Implicit differentiation of tensor network algorithms ¨Ostlund and S

Reference 17

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Observation d767e5f3-009e-4985-92f4-69985d8db906 · outbound

This paper cites Renormalization algorithms for Quantum-Many Body Systems in two and higher dimensions.

Implicit differentiation of tensor network algorithms Renormalization algorithms for Quantum-Many Body Systems in two and higher dimensions

Reference 18

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Observation 050d583f-95c7-4535-93bb-2ba28a468265 · outbound

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Implicit differentiation of tensor network algorithms Unresolved cited work

Reference 20

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Observation 33f722a5-3394-47bf-99c8-fd4712ea0260 · outbound

This paper cites Corboz, Variational optimization with infinite pro- jected entangled-pair states, Phys.

Implicit differentiation of tensor network algorithms Corboz, Variational optimization with infinite pro- jected entangled-pair states, Phys

Reference 21

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Observation f113c773-8f54-42de-981c-6be26bd2e87d · outbound

This paper cites Vanderstraeten, J.

Implicit differentiation of tensor network algorithms Vanderstraeten, J

Reference 22

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Observation fe20840d-fafc-40cd-8259-f0746739c219 · outbound

This paper cites Nishino and K.

Implicit differentiation of tensor network algorithms Nishino and K

Reference 23

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Observation 1f384314-1740-4a1f-81a9-4005d651017c · outbound

This paper cites Or´ us and G.

Implicit differentiation of tensor network algorithms Or´ us and G

Reference 24

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Observation 02d1247f-1e02-4a73-9435-a06dca7b77a7 · outbound

This paper cites Jordan, R.

Implicit differentiation of tensor network algorithms Jordan, R

Reference 25

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Observation 6cf496b5-2244-4f5f-b97f-0dececce95aa · outbound

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Implicit differentiation of tensor network algorithms Haegeman and F

Reference 26

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Observation ae6d8620-4287-4bd9-bdd0-570b6b82516d · outbound

This paper cites Zauner-Stauber, L.

Implicit differentiation of tensor network algorithms Zauner-Stauber, L

Reference 27

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Observation 2f6a4467-2ec1-40e4-ba9e-1f58a95a54e0 · outbound

This paper cites Liao, J.-G.

Implicit differentiation of tensor network algorithms Liao, J.-G

Reference 28

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Observation 81f78a98-2e5e-4fb2-b042-83a9662cf0cc · outbound

This paper cites Hasik, D.

Implicit differentiation of tensor network algorithms Hasik, D

Reference 29

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Observation aec1f60d-c7f1-4969-a234-77a6bc752091 · outbound

This paper cites Ponsioen, F.

Implicit differentiation of tensor network algorithms Ponsioen, F

Reference 30

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Observation 8ab23b04-5cb4-49c8-9418-53a75ae8d49e · outbound

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Implicit differentiation of tensor network algorithms Unresolved cited work

Reference 31

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Observation 7459691d-1677-4d06-8781-d5f135c4df7b · outbound

This paper cites Zhang, S.

Implicit differentiation of tensor network algorithms Zhang, S

Reference 32

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Observation 98757c5e-0391-46cc-a176-09bfbe00af86 · outbound

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Implicit differentiation of tensor network algorithms Unresolved cited work

Reference 33

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Observation 3ac04b53-0e0e-4bfc-9f2f-fcb7ac063bae · outbound

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Implicit differentiation of tensor network algorithms Unresolved cited work

Reference 34

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Observation e47b4d53-b8e2-436c-8cd3-e7f5b26c7b47 · outbound

This paper cites Simulating fermionic fractional Chern insulators with infinite projected entangled-pair states.

Implicit differentiation of tensor network algorithms Simulating fermionic fractional Chern insulators with infinite projected entangled-pair states

Reference 35

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Observation b99f3a80-cf71-430a-9901-3f940b401cfa · outbound

This paper cites Christianson, Reverse accumulation and attractive fixed points, Optim.

Implicit differentiation of tensor network algorithms Christianson, Reverse accumulation and attractive fixed points, Optim

Reference 36

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Observation b6e61abe-d053-4128-9d43-05bd8fda7792 · outbound

This paper cites Naumann, E.

Implicit differentiation of tensor network algorithms Naumann, E

Reference 37

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Observation 0e23eced-8cf0-4eab-9bab-f622a6fadeda · outbound

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Implicit differentiation of tensor network algorithms Francuz, N

Reference 38

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Observation c807fcea-3ade-4e80-bd77-364ced0091ac · outbound

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Implicit differentiation of tensor network algorithms Unresolved cited work

Reference 39

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Observation c213f3cb-e7d0-4886-a8af-b29224673197 · outbound

This paper cites Efficient and Modular Implicit Differentiation.

Implicit differentiation of tensor network algorithms Efficient and Modular Implicit Differentiation

Reference 40

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Observation a6e05642-1dd1-434b-aca4-07504fa13a24 · outbound

This paper cites Nocedal and S.

Implicit differentiation of tensor network algorithms Nocedal and S

Reference 41

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Observation e3ef6130-e06c-4419-82dd-369c9b8d76f0 · outbound

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Implicit differentiation of tensor network algorithms Unresolved cited work

Reference 42

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Observation b10201bb-c1d7-4924-b73f-73056c094b8e · outbound

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Implicit differentiation of tensor network algorithms Unresolved cited work

Reference 43

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Observation f4d5154b-6fa0-46f2-a9e1-822fff7e92d2 · outbound

This paper cites Vanderstraeten, L.

Implicit differentiation of tensor network algorithms Vanderstraeten, L

Reference 44

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Observation 1790a64e-a9a0-4388-ab7f-c9b4f5924fae · outbound

This paper cites Nietner, B.

Implicit differentiation of tensor network algorithms Nietner, B

Reference 45

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Observation 58d43cdf-5697-4890-a2c9-5e4a52da366a · outbound

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Implicit differentiation of tensor network algorithms Hauru, M

Reference 46

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Observation 7f6c777d-8f8e-4626-97ab-72cf3ce20db0 · outbound

This paper cites Accelerating two-dimensional tensor network contractions using QR decompositions.

Implicit differentiation of tensor network algorithms Accelerating two-dimensional tensor network contractions using QR decompositions

Reference 47

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Observation 89dec32e-fe55-4064-906f-21d054a1a585 · outbound

This paper cites Hodgkiss, L.

Implicit differentiation of tensor network algorithms Hodgkiss, L

Reference 48

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Observation 57753f52-8e0b-4193-89e5-0fd9a22ae2d4 · outbound

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Implicit differentiation of tensor network algorithms Vanhecke, M

Reference 49

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Observation db309eed-6766-4332-8570-31929adb812c · outbound

This paper cites Vanderstraeten, J.

Implicit differentiation of tensor network algorithms Vanderstraeten, J

Reference 50

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Observation 2c7530ca-57a5-474e-a94c-51334f115272 · outbound

This paper cites Corboz, J.

Implicit differentiation of tensor network algorithms Corboz, J

Reference 51

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Observation ab787c33-c80b-46f1-9045-b23905e40e9a · outbound

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Implicit differentiation of tensor network algorithms PEPSKit.jl: A Julia package for projected entangled-pair state simulations

Reference 52

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Observation cc33e0e3-2f64-4d8e-aca7-cbb62d9f83cd · outbound

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Implicit differentiation of tensor network algorithms Devos, M

Reference 53

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Observation 1cf3cef1-386c-47ad-a46d-e2f53ab6991d · outbound

This paper cites TensorKit.jl: A Julia package for large-scale tensor computations, with a hint of category theory.

Implicit differentiation of tensor network algorithms TensorKit.jl: A Julia package for large-scale tensor computations, with a hint of category theory

Reference 54

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Observation 366d42ec-97ac-463b-93cc-52d1935fa555 · outbound

This paper cites Burgelman and P.

Implicit differentiation of tensor network algorithms Burgelman and P

Reference 55

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Observation 029b21a9-aeb3-4137-886e-2c6b56ac3e46 · outbound

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Implicit differentiation of tensor network algorithms Unresolved cited work

Reference 56

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Observation bd76739d-7977-4e6e-9ea0-edd4faced88b · outbound

This paper cites Vanderstraeten, J.

Implicit differentiation of tensor network algorithms Vanderstraeten, J

Reference 57

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Observation 9a79f332-7b37-45da-8065-7c9a5e67680b · outbound

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Implicit differentiation of tensor network algorithms NCON: A tensor network contractor for MATLAB

Reference 58

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Observation a8a1f7ae-399b-4b32-8d4d-eaf18d6b1a37 · outbound

This paper cites This approach is entirely equivalent, and can even be implemented by only tracking the ad- joint variables as defined above.

Implicit differentiation of tensor network algorithms This approach is entirely equivalent, and can even be implemented by only tracking the ad- joint variables as defined above

Reference 59

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Observation 9f55991c-9098-46bc-b33c-4e0f946a1861 · outbound

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Implicit differentiation of tensor network algorithms Unresolved cited work

Reference 60

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Observation 8fa8e724-92be-4c5e-b218-6573eea51027 · outbound

This paper cites Xie, J.-G.

Implicit differentiation of tensor network algorithms Xie, J.-G

Reference 61

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Observation 9487c5d8-f54e-4aee-8358-240beb6b8eb9 · outbound

This paper cites Corboz, S.

Implicit differentiation of tensor network algorithms Corboz, S

Reference 62

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Observation 502c34b2-9b9a-42f0-809a-d3c4af5b4c92 · outbound

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Implicit differentiation of tensor network algorithms Unresolved cited work

Reference 63

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Pith citing papers

Observation 0b9fcb2e-9246-4dcc-8b56-54e691a51139 · inbound

On the origin of finite entanglement scaling cites this paper.

On the origin of finite entanglement scaling Implicit differentiation of tensor network algorithms

Reference 23

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