Paper Citation Record · LEDGER

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization

As of 12 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 1 inbound Pith citation observation for arXiv:2510.24755.

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

pith.paper-citation-record.v1
2510.24755 v2

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T09:07:15.309220Z

measured 66 of 66 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T09:07:06.177824Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

65 of 65 outbound references displayed

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External citation measurements

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Outbound references

Observation cb75ec58-60ca-4083-93ea-3830e2e7ced2 · outbound

This paper cites To do so: First, draw a sampleIofnindices from the uniform dis- tribution.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization To do so: First, draw a sampleIofnindices from the uniform dis- tribution

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T09:07:06.397501Z

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Observation 615755ea-5ca8-44a5-890b-6d5cd1a816b1 · outbound

This paper cites an unresolved cited work.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Unresolved cited work

Reference 2

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Observation 9852f9b4-a139-43ac-9add-51070faddcd2 · outbound

This paper cites We callythe sketch vector and it contains an em- pirical estimation ofgeneralized momentsof the dis- tributionπ.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization We callythe sketch vector and it contains an em- pirical estimation ofgeneralized momentsof the dis- tributionπ

Reference 3

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Observation 94b73e48-9ee0-4182-8059-2f25b77569c1 · outbound

This paper cites The decoding procedure is taken from com- pressive sensinggreedy methods(Matching pur- suit, Orthogonal Matching Pursuit.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization The decoding procedure is taken from com- pressive sensinggreedy methods(Matching pur- suit, Orthogonal Matching Pursuit

Reference 4

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Observation b2d556eb-1293-4fa5-9112-a7207c43a0cd · outbound

This paper cites The Monte-Carlo method [17] can be used to approx- imate generalized moments similar to the one from [16].

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization The Monte-Carlo method [17] can be used to approx- imate generalized moments similar to the one from [16]

Reference 5

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Observation b01e9319-413d-4d72-83cf-96a83860dee5 · outbound

This paper cites Kadowaki and H.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Kadowaki and H

Reference 6

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Observation 6489ca23-6080-4712-85fe-79a386f7b871 · outbound

This paper cites Robbins and S.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Robbins and S

Reference 7

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Observation 1974aa21-b8de-4e44-96bb-536c05b67d71 · outbound

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A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Unresolved cited work

Reference 8

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Observation e20593a9-9824-490f-af8c-9506eb2dc0cf · outbound

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A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Unresolved cited work

Reference 9

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Observation c9c7af06-325d-4baf-a99b-726f1a161a4e · outbound

This paper cites Lucas, Frontiers in Physics2(2014), 10.3389/fphy.2014.00005, arXiv:1302.5843 [cond-mat].

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Lucas, Frontiers in Physics2(2014), 10.3389/fphy.2014.00005, arXiv:1302.5843 [cond-mat]

Reference 10

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Observation 9e1e842d-ab35-4293-b5ad-25451492285c · outbound

This paper cites Kirkpatrick, C.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Kirkpatrick, C

Reference 11

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Observation dda4b296-1f36-46c8-8fab-ad56e74b091b · outbound

This paper cites Candes, J.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Candes, J

Reference 12

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Observation 65b27cd6-7de1-43bb-ba5c-10084a66e885 · outbound

This paper cites Quantum annealing: An introduction and new developments.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Quantum annealing: An introduction and new developments

Reference 13

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Observation c5844182-9179-478d-86df-e48d17277107 · outbound

This paper cites Bellman, Bulletin of the American Mathematical So- ciety60, 503 (1954).

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Bellman, Bulletin of the American Mathematical So- ciety60, 503 (1954)

Reference 14

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Observation d5b55a29-2a3d-44ec-a7cc-622589bfb61f · outbound

This paper cites Barahona, Journal of Physics A: Mathematical and General15, 3241 (1982).

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Barahona, Journal of Physics A: Mathematical and General15, 3241 (1982)

Reference 15

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Observation 65a13350-7456-487a-b3f8-502771b24362 · outbound

This paper cites A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization

Reference 16

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Observation 34c295db-3179-48a8-bcbc-ef712329988a · outbound

This paper cites Schuch and J.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Schuch and J

Reference 17

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Observation 095cef9b-ee0f-41bb-9aa4-1ef0eeb032e1 · outbound

This paper cites Delahaye, S.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Delahaye, S

Reference 18

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Observation 962f6e91-2863-4faf-b417-2dd750cbee50 · outbound

This paper cites Donoho, IEEE Transactions on Information Theory 52, 1289 (2006).

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Donoho, IEEE Transactions on Information Theory 52, 1289 (2006)

Reference 19

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Observation 3dbde2b5-065b-47bc-96a5-262b59034af4 · outbound

This paper cites Baraniuk, M.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Baraniuk, M

Reference 20

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Observation e6736e18-4375-4c0c-880a-8642c774ffbe · outbound

This paper cites Foucart and H.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Foucart and H

Reference 21

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Observation 89fed322-2439-4695-9c85-4ffe98bce073 · outbound

This paper cites Compressive Statistical Learning with Random Feature Moments.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Compressive Statistical Learning with Random Feature Moments

Reference 22

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Observation dae637f1-4415-4f45-93a7-d66e077aab8f · outbound

This paper cites Metropolis and S.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Metropolis and S

Reference 23

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Observation cd76f9fa-4b43-4d59-aeca-d6cfbf35885f · outbound

This paper cites Metropolis, A.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Metropolis, A

Reference 24

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Observation c3a637ad-9810-432e-a7ca-d5ab164a7dc0 · outbound

This paper cites Xiang, D.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Xiang, D

Reference 25

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Observation 806f7025-051c-47bb-82fc-6ed624e98d07 · outbound

This paper cites Tsallis, Journal of Statistical Physics52, 479 (1988).

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Tsallis, Journal of Statistical Physics52, 479 (1988)

Reference 26

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Observation 356900e1-0aa5-414a-8fa4-897fa43c294b · outbound

This paper cites Tsallis and D.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Tsallis and D

Reference 27

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Observation 63864ef7-4e9f-4765-b2ce-d575e34fc00d · outbound

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A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Unresolved cited work

Reference 28

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Observation d180fb7c-2f85-468a-834f-8bf5ec7e6968 · outbound

This paper cites Chevalier, W.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Chevalier, W

Reference 29

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Observation 377bf80f-0dac-4a15-b1c5-34b5905072a9 · outbound

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A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Unresolved cited work

Reference 30

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Observation ed76c287-7c87-4c43-b94e-c44ccd2d6fea · outbound

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A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Unresolved cited work

Reference 31

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Observation 930cf4f3-f645-498a-a545-32cf37563b5f · outbound

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A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Unresolved cited work

Reference 32

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Observation 96db1fe8-a319-4573-a1cf-6bea80796009 · outbound

This paper cites NP-complete Problems and Physical Reality.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization NP-complete Problems and Physical Reality

Reference 33

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Observation ed586deb-d9da-4f4a-9a99-23af49b98047 · outbound

This paper cites Quantum Computation by Adiabatic Evolution.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Quantum Computation by Adiabatic Evolution

Reference 34

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Observation 490f8bf3-4ba9-4222-a613-3627d3ac06df · outbound

This paper cites A Quantum Approximate Optimization Algorithm.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization A Quantum Approximate Optimization Algorithm

Reference 35

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Observation 56220648-fc78-46da-930d-bce14f6bd075 · outbound

This paper cites Zhou, S.-T.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Zhou, S.-T

Reference 36

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Observation d969b213-8248-480f-a7b6-d9361c244726 · outbound

This paper cites Park and N.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Park and N

Reference 37

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Observation eec04d3e-ed30-47c6-bd38-0111c6cd0f51 · outbound

This paper cites McArdle, T.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization McArdle, T

Reference 38

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Observation 1f93f7a7-7884-44c3-9ee3-5dbe5109e738 · outbound

This paper cites Dissipative ground state preparation and the Dissipative Quantum Eigensolver.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Dissipative ground state preparation and the Dissipative Quantum Eigensolver

Reference 39

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Observation 50c1ad60-55f7-4238-b468-715492465b18 · outbound

This paper cites Rapid quantum ground state preparation via dissipative dynamics,.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Rapid quantum ground state preparation via dissipative dynamics,

Reference 40

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Observation c7c3caa6-c536-4b05-8bbd-98909f92517a · outbound

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A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Unresolved cited work

Reference 41

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Observation 6098c1f0-4d3e-44e4-b18b-3cd9aa0e4799 · outbound

This paper cites The probability in that string to find 001 after 00 is given by 1 L.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization The probability in that string to find 001 after 00 is given by 1 L

Reference 42

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Observation 9d49da65-6095-4b5a-b785-64c855ef5b40 · outbound

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A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Unresolved cited work

Reference 43

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Observation d1bfa091-b061-4633-bd65-34789cf9f4d8 · outbound

This paper cites We want to count all the strings of a given lengthNwhich contains at least one ‘001‘ substring.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization We want to count all the strings of a given lengthNwhich contains at least one ‘001‘ substring

Reference 44

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Observation 88e68c96-0b6b-4c76-9bc1-ef18748db834 · outbound

This paper cites Because form∈S N , ifmend by ‘00‘ the othersN−2 bits are free and should contains ‘001‘ a single time.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Because form∈S N , ifmend by ‘00‘ the othersN−2 bits are free and should contains ‘001‘ a single time

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-04T09:07:12.384433Z

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Observation a0789616-65d8-41c5-910b-72dc38157af6 · outbound

This paper cites Because the last 3 bits are fixed to ‘001‘ the remainingN−2 bits are free and should not contain any ‘001‘.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Because the last 3 bits are fixed to ‘001‘ the remainingN−2 bits are free and should not contain any ‘001‘

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-04T09:07:12.508613Z

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Observation f24f874d-6817-41bf-98b1-6fc29e920f81 · outbound

This paper cites The goal is to compute the its cardinalityz L = #ZL.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization The goal is to compute the its cardinalityz L = #ZL

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-04T09:07:12.637824Z

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Observation 45449145-0c44-4d3e-8bfe-430333138af4 · outbound

This paper cites an unresolved cited work.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Unresolved cited work

Reference 48

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Observation 6b436cb3-8075-4a76-a2e2-911b88dc1d2d · outbound

This paper cites Case 2: Assume we have two zeros starting in positionN−1 so we have two 00 substrings in total.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Case 2: Assume we have two zeros starting in positionN−1 so we have two 00 substrings in total

Reference 49

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Observation 4df7395a-961c-45bd-bea7-4c61a0771a73 · outbound

This paper cites an unresolved cited work.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Unresolved cited work

Reference 50

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Observation 0172a448-fe51-4234-811b-38eb65999715 · outbound

This paper cites Case 1: Assume we haveL−1 zeros in positionsk−Ltok−1 so we have exactlyLsubstrings equals 00.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Case 1: Assume we haveL−1 zeros in positionsk−Ltok−1 so we have exactlyLsubstrings equals 00

Reference 51

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Observation 814eedd4-da5b-47aa-9444-0e8aa3dcb73c · outbound

This paper cites 0| {z } L−1 001·w 2 where: •w 1 ∈A ∗ k−L •w 2 ∈A N−(k+2) So the number of all such stringssisa ∗ k−LaN−(k+2) =f k−L+1fN−k.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization 0| {z } L−1 001·w 2 where: •w 1 ∈A ∗ k−L •w 2 ∈A N−(k+2) So the number of all such stringssisa ∗ k−LaN−(k+2) =f k−L+1fN−k

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-04T09:07:13.366568Z

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Observation 695d1308-0225-4e74-a7b0-5cd2ec98dd45 · outbound

This paper cites 0| {z } L where: 20 •w 1 ∈A ∗ k−1 •w 2 ∈A ∗ N−L−k−2 So the number of all such stringssisa ∗ k−1a∗ N−L−k−2 =f kfN−L−k−1.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization 0| {z } L where: 20 •w 1 ∈A ∗ k−1 •w 2 ∈A ∗ N−L−k−2 So the number of all such stringssisa ∗ k−1a∗ N−L−k−2 =f kfN−L−k−1

Reference 53

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Observation c64c5b0e-213e-4dc7-87f0-40b75a6d69df · outbound

This paper cites |{z} k−L.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization |{z} k−L

Reference 54

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Observation 26442288-d7f6-42b5-a461-e6e6312a0184 · outbound

This paper cites |{z} k−L.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization |{z} k−L

Reference 55

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Observation 9f96fafc-1ab3-4064-8155-c63f4315c62f · outbound

This paper cites | {z } k−L 00 ↑ k.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization | {z } k−L 00 ↑ k

Reference 56

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Observation 7cd9eaa1-2027-49fe-80d5-78d7e9659ea3 · outbound

This paper cites | {z } k−1 0 ↑ k.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization | {z } k−1 0 ↑ k

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-04T09:07:14.079171Z

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Observation e4fc54db-43ba-4e03-ba69-6fae71afe8e2 · outbound

This paper cites an unresolved cited work.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Unresolved cited work

Reference 58

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Observation 2d881bcb-b53b-4e91-92b0-77c3005561aa · outbound

This paper cites an unresolved cited work.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Unresolved cited work

Reference 59

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Observation c7c53f1e-f6b4-4737-ac1d-9a027c749683 · outbound

This paper cites an unresolved cited work.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Unresolved cited work

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-04T09:07:14.531611Z

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Observation 2ad9bc46-fbb4-4c26-87e4-f1a0b08be809 · outbound

This paper cites an unresolved cited work.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Unresolved cited work

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-04T09:07:14.669914Z

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Observation 45b69e25-82f8-4802-b58c-4182340c411e · outbound

This paper cites an unresolved cited work.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Unresolved cited work

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-04T09:07:14.842125Z

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Observation 0a3c6791-b612-42ab-8606-435b8c92ce26 · outbound

This paper cites ∥FN (a)−F N (b)∥2 2 =FN (a)·F N (a) +F N (b)·F N (b)−2F N (a)·F N (b) Thus, we will analyzeF N (a)·F N (b).

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization ∥FN (a)−F N (b)∥2 2 =FN (a)·F N (a) +F N (b)·F N (b)−2F N (a)·F N (b) Thus, we will analyzeF N (a)·F N (b)

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-04T09:07:15.019951Z

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Observation 1f14b378-9fc3-4d36-b5e0-e314818cfd70 · outbound

This paper cites an unresolved cited work.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization Unresolved cited work

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-04T09:07:15.139090Z

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Observation 480d2d5b-708c-4a71-b5f8-5545a91e766b · outbound

This paper cites The 2N+1 length vector indicating the position of (N+ 1)-bit strings which start fromacan be written asa⊗1 2N−k+1 , since there are 2 N+1−k strings like that.

A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization The 2N+1 length vector indicating the position of (N+ 1)-bit strings which start fromacan be written asa⊗1 2N−k+1 , since there are 2 N+1−k strings like that

Reference 65

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Pith citing papers

Observation 65a13350-7456-487a-b3f8-502771b24362 · inbound