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Paper Citation Record · LEDGER

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits

As of 10 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 2 inbound Pith citation observations for arXiv:2512.19623.

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

pith.paper-citation-record.v1
2512.19623 v2

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T14:50:33.167332Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T14:05:00.410672Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-04T00:49:18.709410Z

Reference resolution

24 of 24 outbound references displayed

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External citation measurements

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Outbound references

Observation 2fe03c38-d7ea-4c9b-bfd8-3affe0df9ab6 · outbound

This paper cites an unresolved cited work.

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits Unresolved cited work

Reference 1

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T14:50:33.076442Z digest=sha256:1d619ed7c5fe2478b7f2aa63fa02ba38fca71c475786fd061339d6ced59ab420

Observation 6750d48d-b28c-46d0-a4ff-ced3e018faa1 · outbound

This paper cites Denote byO Φ := Φ†(O)the corresponding effective observable, and let ˜OΦ be its Hermitian approximation satisfying∥ ˜OΦ −O Φ∥∞ ≤ϵ.

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits Denote byO Φ := Φ†(O)the corresponding effective observable, and let ˜OΦ be its Hermitian approximation satisfying∥ ˜OΦ −O Φ∥∞ ≤ϵ

Reference 2

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source=pdf_text observed=2026-08-03T14:50:33.081551Z digest=sha256:baff7f9bdef0e288a850546acedca2a0ca0d7f601b9134c699251873acf60c42

Observation 13d0563d-643f-4b44-8c6b-5253c7180b5c · outbound

This paper cites Denote byO Φ := Φ†(O)the corresponding effective observable, and let ˜OΦ be its Hermitian approximation satisfying∥ ˜OΦ − OΦ∥∞ ≤ϵ.

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits Denote byO Φ := Φ†(O)the corresponding effective observable, and let ˜OΦ be its Hermitian approximation satisfying∥ ˜OΦ − OΦ∥∞ ≤ϵ

Reference 3

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source=pdf_text observed=2026-08-03T14:50:33.086508Z digest=sha256:f3d802824ae2a3b207e6c69829f3c1d305ed8f6cc30ee3c6f47cfae30663ab46

Observation cc0bbe96-ed0a-4c5a-8e34-17e3e1411914 · outbound

This paper cites an unresolved cited work.

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits Unresolved cited work

Reference 4

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source=pdf_text observed=2026-08-03T14:50:33.090563Z digest=sha256:e310f1cf637e485c71b7a98a75b121188a31ef81ae0d0cbc7ae44b0f60a27e77

Observation eb41212e-f7b4-45a8-86ca-21550e59e823 · outbound

This paper cites an unresolved cited work.

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits Unresolved cited work

Reference 5

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source=pdf_text observed=2026-08-03T14:50:33.094770Z digest=sha256:47ea8e08e24d393ef33ea78a7d92531282c7485d1ab46a75fcfb4f3c6c72c6e1

Observation 18020288-57e0-4f09-b938-0a85cfe6cb4c · outbound

This paper cites Theorem 4 (Performance guarantee for ˆOΦ,x).LetΦ :L(C din )→L(C dout )be an unknown CPTP map, and letO∈H(C dout )be a known Hermitian operator.

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits Theorem 4 (Performance guarantee for ˆOΦ,x).LetΦ :L(C din )→L(C dout )be an unknown CPTP map, and letO∈H(C dout )be a known Hermitian operator

Reference 6

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source=pdf_text observed=2026-08-03T14:50:33.099208Z digest=sha256:aa3aaa3b5a35799dccdf8c532b7fd6a708076707c8774893f91e4e26360032aa

Observation ddaa5df7-cf60-4a80-9f0b-57bdc2e89d10 · outbound

This paper cites L[ i=1 Ei # ≤ LX i=1 Pr(Ei).(S47) In our analysis, we often use the following equivalent inequality obtained by applying De Morgan’s law: 1− LX i=1 Pr [Ec i ]≤Pr.

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits L[ i=1 Ei # ≤ LX i=1 Pr(Ei).(S47) In our analysis, we often use the following equivalent inequality obtained by applying De Morgan’s law: 1− LX i=1 Pr [Ec i ]≤Pr

Reference 7

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source=pdf_text observed=2026-08-03T14:50:33.103603Z digest=sha256:1d449f994b9091b91f094153e69d0db1eb706fd51e96da2bbcb0190cbfce9a1c

Observation 6d414332-3cea-4e67-a42a-80a6511e0e0d · outbound

This paper cites RO k=1 M(k) apr(Mk) ! ρ # −tr.

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits RO k=1 M(k) apr(Mk) ! ρ # −tr

Reference 8

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source=pdf_text observed=2026-08-03T14:50:33.107396Z digest=sha256:e5351ba16795ae9597b8a51da6fdc5fbdcf27dc0f2ea17fd214c46a213772918

Observation 6108e9bc-195e-4dd4-8790-088586ef3bea · outbound

This paper cites Assume that we have access only to devices whose size is bounded by the maximal number of qubits required to representρandΦ (i1,...,il) for each l= 1, ..., L.

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits Assume that we have access only to devices whose size is bounded by the maximal number of qubits required to representρandΦ (i1,...,il) for each l= 1, ..., L

Reference 9

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source=pdf_text observed=2026-08-03T14:50:33.111204Z digest=sha256:9687265f2194925244bf66310b573d8ebdc5e5f4963949eee3d172a7c3085f8f

Observation 8efb8815-fb1d-4312-97bd-9b210fb1ce65 · outbound

This paper cites RO i1=1 ˜Mi1 ρ # ≤ϵ 0.(S474) Similarly, it is sufficient to take ϵ0 = ϵ 2 ,(S475) by the triangle inequality, ˆµ−tr.

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits RO i1=1 ˜Mi1 ρ # ≤ϵ 0.(S474) Similarly, it is sufficient to take ϵ0 = ϵ 2 ,(S475) by the triangle inequality, ˆµ−tr

Reference 10

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source=pdf_text observed=2026-08-03T14:50:33.114916Z digest=sha256:4c782ca6b14f15e06a7b024f88707d077166fec3d1a35ba68df2b52093646452

Observation 46a3a0f2-a698-48e0-b0e4-fb03a13f9fbb · outbound

This paper cites First, we summarize the good-tomography event for the learning procedure at each depth.

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits First, we summarize the good-tomography event for the learning procedure at each depth

Reference 11

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source=pdf_text observed=2026-08-03T14:50:33.118726Z digest=sha256:8fcfd11eac1c46d02ec02b65a9f1666c2dc85f4415ca45feebf3c68698fd7d0b

Observation 0f115a98-a197-4593-bb5d-09fabe769d12 · outbound

This paper cites 7 at the wires betweenΦ i andΦ i+1 for i= 0, ..., L−1.

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits 7 at the wires betweenΦ i andΦ i+1 for i= 0, ..., L−1

Reference 12

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source=pdf_text observed=2026-08-03T14:50:33.122431Z digest=sha256:ac2447bd3b2a94b4e98f50179bf655b44e162940642619636d6d5863ef9923ad

Observation df9319df-27fd-4454-b57c-29d9a3dd6f62 · outbound

This paper cites an unresolved cited work.

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits Unresolved cited work

Reference 13

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source=pdf_text observed=2026-08-03T14:50:33.126090Z digest=sha256:17fd50705e4d20298422ce25bc55723472eb74df2f93456cdfb6d04c5f64c7c3

Observation bfb6248f-dc77-422c-98d2-e5401cbff49c · outbound

This paper cites Ex[τ ⊗2 x ] RO r=1 EUr U † r |vr ηi,s⟩ ⟨vr ηi,s|U r ⊗2 # (S521) =p 2 ηi ω2 ηi,sd2Rtr.

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits Ex[τ ⊗2 x ] RO r=1 EUr U † r |vr ηi,s⟩ ⟨vr ηi,s|U r ⊗2 # (S521) =p 2 ηi ω2 ηi,sd2Rtr

Reference 14

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Observation 8041a3e7-31c3-4b4d-ae71-4f1b394a2d59 · outbound

This paper cites an unresolved cited work.

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits Unresolved cited work

Reference 15

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source=pdf_text observed=2026-08-03T14:50:33.134417Z digest=sha256:7489c3f9156e2ff09b963bdbc68fc4474d1c30c51dfb5c6006fefe2a3011c3db

Observation c66aec8a-389a-40b3-a40a-f99b6fa1b7b6 · outbound

This paper cites In the following, we briefly review these two approaches and explain how our results relate to the existing methods.

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits In the following, we briefly review these two approaches and explain how our results relate to the existing methods

Reference 16

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Observation 73d68d47-9b64-4dbf-849e-bbb364328720 · outbound

This paper cites an unresolved cited work.

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits Unresolved cited work

Reference 17

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Observation 466447d1-abe4-45c0-9102-7308dcc65a30 · outbound

This paper cites an unresolved cited work.

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits Unresolved cited work

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Observation 344520d4-486b-4499-8139-2ad921d8815f · outbound

This paper cites an unresolved cited work.

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits Unresolved cited work

Reference 20

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Observation 03489dbe-3ffb-4dc8-a799-cb13c2dd490f · outbound

This paper cites an unresolved cited work.

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits Unresolved cited work

Reference 21

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source=pdf_text observed=2026-08-03T14:50:33.152464Z digest=sha256:2887653caf641d935bd65bf29be5526adee4839b28b169d6e6563b0188ff9a2c

Observation cdd87fa6-7652-4c35-8166-935d5e4c842e · outbound

This paper cites an unresolved cited work.

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits Unresolved cited work

Reference 22

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source=pdf_text observed=2026-08-03T14:50:33.155839Z digest=sha256:46a4535a6b27f3f45c0a75664757ecfe30fb157d55bd33b96a95e3f46e3fb1b5

Observation b71f76d2-9cc0-487e-af88-0fa6b1cd22c5 · outbound

This paper cites LO” denotes local operations without classical communication, whereas “LOCC.

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits LO” denotes local operations without classical communication, whereas “LOCC

Reference 23

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source=pdf_text observed=2026-08-03T14:50:33.159798Z digest=sha256:45bfa65e1c010fd67bd2c1d3ef417154e0cd4c5015ee55301cc1a2a7a777a205

Observation 53f8c617-3402-444c-bf50-9c44e742f96c · outbound

This paper cites In this subsection, we focus on wire cuts, which are most relevant to our work, and discuss how existing constructions relate to our results.

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits In this subsection, we focus on wire cuts, which are most relevant to our work, and discuss how existing constructions relate to our results

Reference 24

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Observation 8582ff3e-93e9-46ff-b232-e3154c47b85a · outbound

This paper cites Below, we summarize the main prior works [113–115], and show that these works can be reformulated in the language of expectation-value-level QPDs.

Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits Below, we summarize the main prior works [113–115], and show that these works can be reformulated in the language of expectation-value-level QPDs

Reference 25

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Pith citing papers

Observation 0d417c14-d1a9-4693-ae52-5010a7dd5a14 · inbound

Scalable quantum circuit knitting using a weak-coupling approximation cites this paper.

Scalable quantum circuit knitting using a weak-coupling approximation Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits

Reference 17

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source=pdf_text observed=2026-06-26T20:57:02.576483Z digest=sha256:89c9166ac17da1a8d5fbeb5b49b2d8383b418f98865f995b825b9f9c16ed1580

Observation fc63e5bd-7c37-4a9a-9d37-1fc88beede29 · inbound

MOSAIQC: Mixed-topology-aware Optimization for Scalable Approximate noise-Informed Quantum circuit Cutting cites this paper.

MOSAIQC: Mixed-topology-aware Optimization for Scalable Approximate noise-Informed Quantum circuit Cutting Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits

Reference 17

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source=pdf_text observed=2026-08-01T14:05:00.410672Z digest=sha256:4c5b7f22219ad27ece0a02536eb7fb7e30f01a6c92f65027f26c8a01902eb89f