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Paper Citation Record · LEDGER

Chebyshev approximation and composition of functions in matrix product states for quantum-inspired numerical analysis

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2407.09609.

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

pith.paper-citation-record.v1
2407.09609 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T21:31:03.018176Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 2290d635-a89e-4546-b1ec-75412c34227e · inbound

Entanglement scaling in matrix product state representation of smooth functions and their shallow quantum circuit approximations cites this paper.

Entanglement scaling in matrix product state representation of smooth functions and their shallow quantum circuit approximations Chebyshev approximation and composition of functions in matrix product states for quantum-inspired numerical analysis

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-05-23T07:42:44.171947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-23T07:38:39.266446Z digest=sha256:2568e90ac7b45f7be5eb5304975e4b0f538a9ffd8d49c46667ee9287dedc6f58

Observation c325e44d-77c9-4775-893d-f4ac6d4292a1 · inbound

Solving the Gross-Pitaevskii equation on multiple different scales using the quantics tensor train representation cites this paper.

Solving the Gross-Pitaevskii equation on multiple different scales using the quantics tensor train representation Chebyshev approximation and composition of functions in matrix product states for quantum-inspired numerical analysis

Reference 68

Resolution
metadata mismatch
arxiv_id, observed 2026-05-19T06:52:07.674526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-05-19T06:49:58.593083Z digest=sha256:4e24ff494bc08a53e7c2e7062c2cb64d3bf82f84cf40fadead315589c904054f

Observation 2f34e001-d836-4bdd-a563-05b0dd586115 · inbound

Tensor-network approach to quantum optical state evolution beyond the Fock basis cites this paper.

Tensor-network approach to quantum optical state evolution beyond the Fock basis Chebyshev approximation and composition of functions in matrix product states for quantum-inspired numerical analysis

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-03T21:31:03.018176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T21:31:03.018176Z digest=sha256:aefd70ed4dba1a6a9801f8f16e8d8ee636e73c9ba74bc65e9083d4e49a6370a8

Observation 716207e6-d98e-4d37-804a-140a3c09a04e · inbound

SeeMPS: A Python-based Matrix Product State and Tensor Train Library cites this paper.

SeeMPS: A Python-based Matrix Product State and Tensor Train Library Chebyshev approximation and composition of functions in matrix product states for quantum-inspired numerical analysis

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-03T08:32:00.105571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T08:32:00.105571Z digest=sha256:9a0515d6a00cf1c3d209102b8ec27d0d980c987256a6094549a91d9bcc43918e

Observation 7156e2d1-2171-46e9-9a80-ad70d8e7e2df · inbound

Local tensor-train surrogates for quantum learning models cites this paper.

Local tensor-train surrogates for quantum learning models Chebyshev approximation and composition of functions in matrix product states for quantum-inspired numerical analysis

Reference 57

Resolution
verified exact
arxiv_id, observed 2026-05-11T23:26:18.932560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-07T16:58:36.889900Z digest=sha256:1026b15264d39915430fc465ec38e72f8788ab70d6ffd2f67c627e00b67c90a2