Paper Citation Record · LEDGER

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits

As of 12 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2605.21346.

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

pith.paper-citation-record.v1
2605.21346 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-21T04:26:05.688196Z

measured 39 of 39 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 2026-08-01T22:20:48.105856Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

38 of 38 outbound references displayed

verified exact0
verified fuzzy23
unresolved14
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ca56ffbe-9994-4e4f-a11e-dd53165d97fa · outbound

This paper cites Utilizing the full pipeline from Appendix B, we compute exact points atnq ∈ {12,14} and|α| ∈ {n q/2, nq}across a selection of devices and noise channels affecting quantum data.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits Utilizing the full pipeline from Appendix B, we compute exact points atnq ∈ {12,14} and|α| ∈ {n q/2, nq}across a selection of devices and noise channels affecting quantum data

Reference 1

Resolution: malformed identifier; raw_fallback, observed 2026-05-21T09:54:58.738646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f6125908-2974-4531-a0ac-efad3e240be0 · outbound

This paper cites This motivates the decomposition of the register into: A(α) :={i:α i = 1},P(α) :={i:α i = 0},(D4) with|A|=|α|.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits This motivates the decomposition of the register into: A(α) :={i:α i = 1},P(α) :={i:α i = 0},(D4) with|A|=|α|

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T09:54:58.734570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8a2e7774-8906-4fed-83aa-8e9d7a019763 · outbound

This paper cites feeding terms.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits feeding terms

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T09:54:58.736639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a593cc02-0e03-4923-aa0a-3820d140ea86 · outbound

This paper cites On an active qubit, the relevant local operator is |0⟩ ⟨1|or|1⟩ ⟨0|, both of which acquire a minus sign under conjugation byZ.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits On an active qubit, the relevant local operator is |0⟩ ⟨1|or|1⟩ ⟨0|, both of which acquire a minus sign under conjugation byZ

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T09:54:58.741025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 07561294-0501-4cf7-905c-8a51e7aae867 · outbound

This paper cites For an active qubit, the X- andY-terms map|0⟩ ⟨1|to|1⟩ ⟨0|(feeding terms that average to zero), while theZ-term contributes a minus sign.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits For an active qubit, the X- andY-terms map|0⟩ ⟨1|to|1⟩ ⟨0|(feeding terms that average to zero), while theZ-term contributes a minus sign

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T09:54:58.732611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a2ae289f-6a36-4f73-8c96-d98542acb8d9 · outbound

This paper cites For an active qubit, the only index-preserving contribution comes fromK0, givingK 0 |0⟩ ⟨1|K† 0 = p1−ϵ p |0⟩ ⟨1|.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits For an active qubit, the only index-preserving contribution comes fromK0, givingK 0 |0⟩ ⟨1|K† 0 = p1−ϵ p |0⟩ ⟨1|

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T09:54:58.678324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 860b1c9f-0233-490a-921a-7e9d19c6811a · outbound

This paper cites From Appendix A, the ensemble-averaged accuracy is: AQ(α) :=E f[AQ(α|f)] = 1 2 1 + ¯γ(α) ,(D13) 28 where¯γ(α) = 1 2nq −1 P y′ γy,α.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits From Appendix A, the ensemble-averaged accuracy is: AQ(α) :=E f[AQ(α|f)] = 1 2 1 + ¯γ(α) ,(D13) 28 where¯γ(α) = 1 2nq −1 P y′ γy,α

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T09:54:58.723897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2ba2fa27-610d-4ba1-a18c-887fd875a72b · outbound

This paper cites an unresolved cited work.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-05-21T09:54:58.680333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7bcbd172-3b7c-401b-9f77-f162d5a29c83 · outbound

This paper cites These platforms feature native all-to-all connectivity, achieved through shared motional bus modes or the coherent transport of qubits [63, 64].

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits These platforms feature native all-to-all connectivity, achieved through shared motional bus modes or the coherent transport of qubits [63, 64]

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T09:54:58.711810Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 985c4f54-1a5a-4478-ac1c-21f554d2b53b · outbound

This paper cites an unresolved cited work.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-05-21T09:54:58.690615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4b7f6129-e7ab-4df0-80dc-474cbd3d49c9 · outbound

This paper cites an unresolved cited work.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-05-21T09:54:58.715832Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c7fbcb7f-2b39-4b87-9013-dde978b1a51f · outbound

This paper cites an unresolved cited work.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-05-21T09:54:58.721631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9697ce90-5039-4645-b8ba-7073fd37cb3c · outbound

This paper cites Like superconducting qubits, theygenerallyrelyonplanarnearest-neighborconnectivityviaexchangeinteractions, incurringidenticalSWAP routing penalties [95].

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits Like superconducting qubits, theygenerallyrelyonplanarnearest-neighborconnectivityviaexchangeinteractions, incurringidenticalSWAP routing penalties [95]

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T09:54:58.717757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3079c4df-ae42-4c60-abe5-8c7eb7fb5949 · outbound

This paper cites an unresolved cited work.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-05-21T09:54:58.682355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9d1f59fc-a9f7-49fa-b1cc-84e30c6ec0f9 · outbound

This paper cites In the local-Clifford shadow protocol, each measurement shotk∈ {1.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits In the local-Clifford shadow protocol, each measurement shotk∈ {1

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T09:54:58.713809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9b426785-aaad-4cc5-9c4d-be3f772481ea · outbound

This paper cites an unresolved cited work.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-05-21T09:54:58.671523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bc8a56fa-8015-4a63-bc42-12d1d8813a0d · outbound

This paper cites an unresolved cited work.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-05-21T09:54:58.695015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1248a7bf-1d02-4021-a52a-e64ba7683e37 · outbound

This paper cites If the two masks differ, the corresponding Pauli support sets are disjoint, and the covariance vanishes.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits If the two masks differ, the corresponding Pauli support sets are disjoint, and the covariance vanishes

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T09:54:58.703818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ddeafa47-73ec-4359-9bd1-09599028f903 · outbound

This paper cites Define the canonical representative setS ∆ :={n∈ {0,1} nq :n h(∆) = 0}.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits Define the canonical representative setS ∆ :={n∈ {0,1} nq :n h(∆) = 0}

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T09:54:58.657928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0c3f778d-dabf-4dbb-a106-45867cdfee79 · outbound

This paper cites What remains is only the subtraction term from the mean, yielding: R(∆) n,p =− 4−nq nc δn,p.(F23) Thus the residual pseudo-covariance is diagonal and exponentially small innq.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits What remains is only the subtraction term from the mean, yielding: R(∆) n,p =− 4−nq nc δn,p.(F23) Thus the residual pseudo-covariance is diagonal and exponentially small innq

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T09:54:58.726298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b41ed722-1bbc-43ac-89e6-341b064b6b0c · outbound

This paper cites an unresolved cited work.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-05-21T09:54:58.663874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c04d3f78-97ad-48aa-855d-85aa42f0bb16 · outbound

This paper cites We find that the surrogate accurately reproduces the scaling of the element-wise variances across the system sizes and shot counts relevant to this work.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits We find that the surrogate accurately reproduces the scaling of the element-wise variances across the system sizes and shot counts relevant to this work

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T09:54:58.699772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation caacdc48-7fbe-4467-99e2-43faf52d15a3 · outbound

This paper cites We define the Signal- to-Noise Ratio asSN R= |µ| σ.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits We define the Signal- to-Noise Ratio asSN R= |µ| σ

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T09:54:58.728574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 92361016-f07e-4a51-bdc6-989e62d4ad37 · outbound

This paper cites an unresolved cited work.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-05-21T09:54:58.673994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7b81cdac-1d94-42f8-8018-bf122f4eafd5 · outbound

This paper cites an unresolved cited work.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-05-21T09:54:58.697047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3c6e5f70-63fb-40e0-8542-ac2bd178384b · outbound

This paper cites We partition the qubit indices into the Active SetA(whereαk = 1) and the Passive SetP(whereα k = 0).

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits We partition the qubit indices into the Active SetA(whereαk = 1) and the Passive SetP(whereα k = 0)

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T09:54:58.719888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 31149a30-08ce-4fed-a22f-358e84a23fb8 · outbound

This paper cites an unresolved cited work.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-05-21T09:54:58.705861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 50905c47-196d-4bc5-8f28-c7d24ed6924f · outbound

This paper cites 3 2 w(y⊕α,t) −4 −nq # ,(H6) Ef[Varsh(∆ct |f)] = 1 nc.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits 3 2 w(y⊕α,t) −4 −nq # ,(H6) Ef[Varsh(∆ct |f)] = 1 nc

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T09:54:58.730491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bb3183bd-2b69-4144-a6c4-a6c06bd84a99 · outbound

This paper cites Since the numerical study is performed in the gaugey= 0nq, Hamming shells around the target locationyare indexed simply by the Hamming weight of the intermediate bit stringt.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits Since the numerical study is performed in the gaugey= 0nq, Hamming shells around the target locationyare indexed simply by the Hamming weight of the intermediate bit stringt

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T09:54:58.701830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 166ca66c-0f77-41f1-82c5-5e17c8c86f32 · outbound

This paper cites The model assigns to each sample a score s(x) =w ⊤x+β 0,(H14) wherewis the vector of feature weights andβ0 is an intercept parameter.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits The model assigns to each sample a score s(x) =w ⊤x+β 0,(H14) wherewis the vector of feature weights andβ0 is an intercept parameter

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T09:54:58.660530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7c1b15fc-549c-447f-b8de-f1a66a013cb0 · outbound

This paper cites an unresolved cited work.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-05-21T09:54:58.669086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f5cc97da-f58e-4ce7-bc70-edd0a0c1b125 · outbound

This paper cites an unresolved cited work.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-05-21T09:54:58.707906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f6b0fd65-cdc5-4032-8bb8-a79543e0f3e0 · outbound

This paper cites To recover the true coefficients, we sort the rows of the augmented matrix[Y|b]in descending order based on empirical observation frequencies.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits To recover the true coefficients, we sort the rows of the augmented matrix[Y|b]in descending order based on empirical observation frequencies

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T09:54:58.692740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ff77123d-9305-4b54-9a65-d875d58735e0 · outbound

This paper cites an unresolved cited work.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits Unresolved cited work

Reference 34

Resolution: unresolved; raw_fallback, observed 2026-05-21T09:54:58.655083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 95a809c3-2566-47e2-bf8d-eab7a9639d6a · outbound

This paper cites This yields, for each thresholdT, n k(b) x (T, nq) oB b=1 ,(J10) withB= 1600in the production runs.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits This yields, for each thresholdT, n k(b) x (T, nq) oB b=1 ,(J10) withB= 1600in the production runs

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T09:54:58.666574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a849c3d7-679b-4541-b876-1b0eb78859ea · outbound

This paper cites Equation (J14) is used here as the minimal phenomenological extrapolation ansatz.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits Equation (J14) is used here as the minimal phenomenological extrapolation ansatz

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T09:54:58.676222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d6a29ff6-611c-4fc1-8d10-9ef3aac18971 · outbound

This paper cites The readout contributionϵr enters only through this quantum target, because the classical measure-first estimators themselves do not depend directly on readout noise.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits The readout contributionϵr enters only through this quantum target, because the classical measure-first estimators themselves do not depend directly on readout noise

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T09:54:58.709834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 96d09961-01fe-415d-a727-c3621fa322db · outbound

This paper cites Because the model (J14) is fit independently at each threshold, small violations of this property can appear after interpolation in T.

Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits Because the model (J14) is fit independently at each threshold, small violations of this property can appear after interpolation in T

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T09:54:58.687496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

Observation a2a4f161-1401-456f-867d-c01aca893cd4 · inbound

The Fourier Wall: Why Public Tabular Datasets Refuse Quantum Advantage, and a Certified Recipe for Where It Lives cites this paper.

The Fourier Wall: Why Public Tabular Datasets Refuse Quantum Advantage, and a Certified Recipe for Where It Lives Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T22:20:48.105856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.