Paper Citation Record · LEDGER

From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models

As of 18 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2607.15433.

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

pith.paper-citation-record.v1
2607.15433 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T23:27:05.989373Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

23 of 23 outbound references displayed

verified exact4
verified fuzzy0
unresolved18
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 757f84a9-8df4-4fef-bdf6-0d2043f6de09 · outbound

This paper cites Inherent interpretability provides inherent value in quantum machine learning.

From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models Inherent interpretability provides inherent value in quantum machine learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T23:27:04.630651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa58a48b-249c-465a-a258-531529fa8f29 · outbound

This paper cites Inherently Interpretable Machine Learning: A Contrasting Paradigm to Post-hoc Explainable AI.

From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models Inherently Interpretable Machine Learning: A Contrasting Paradigm to Post-hoc Explainable AI

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-01T23:27:04.677146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e012a79-a722-4166-b5e5-dd6f1272fc07 · outbound

This paper cites Recognizing mechanistic reasoning in student scientific inquiry: A frame- work for discourse analysis developed from philosophy of science.

From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models Recognizing mechanistic reasoning in student scientific inquiry: A frame- work for discourse analysis developed from philosophy of science

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T23:27:04.781425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f08b420f-8f54-49a6-8234-d1de59d34e2b · outbound

This paper cites Bishop.Pattern Recognition and Machine Learning.

From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models Bishop.Pattern Recognition and Machine Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T23:27:04.842334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39045fc5-d1fe-4c09-92c0-2af61d7f9a3d · outbound

This paper cites an unresolved cited work.

From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models Unresolved cited work

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T23:27:04.862085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b73ef79-03d0-428a-8362-c2d148c05fe1 · outbound

This paper cites an unresolved cited work.

From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models Unresolved cited work

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T23:27:04.882917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eedd047d-2b9e-4b26-813a-29f87b954733 · outbound

This paper cites Nielsen and Isaac L.

From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models Nielsen and Isaac L

Reference 7

Resolution: malformed identifier; no resolver link, observed 2026-08-01T23:27:04.913683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0583e71b-a3ba-4616-bdcc-213aba693001 · outbound

This paper cites LIBLINEAR: A Library for Large Linear Classification.

From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models LIBLINEAR: A Library for Large Linear Classification

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T23:27:04.960495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd769165-8ad3-430d-a90e-ad426bd43101 · outbound

This paper cites Deep Residual Learning for Image Recognition.

From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models Deep Residual Learning for Image Recognition

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T23:27:04.997546Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8735d83-863e-4575-9010-fba18b3a6e06 · outbound

This paper cites an unresolved cited work.

From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models Unresolved cited work

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T23:27:05.055370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5a44198-42c2-4e25-8371-beba4f89783f · outbound

This paper cites Understanding intermediate layers using linear classifier probes.

From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models Understanding intermediate layers using linear classifier probes

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T23:27:05.209744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c54875d-4fd5-4c02-ab42-217631a727bb · outbound

This paper cites Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning.

From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T23:27:05.269413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50c325b9-9073-422e-8e1c-ad67be6bedda · outbound

This paper cites Different Scaling of Linear Models and Deep Learning in UK Biobank Brain Images versus Machine-Learning Datasets.

From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models Different Scaling of Linear Models and Deep Learning in UK Biobank Brain Images versus Machine-Learning Datasets

Reference 13

Resolution: verified exact; doi, observed 2026-08-01T23:28:28.540260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 26f9d8fd-f174-4764-90ba-eecd96c31c36 · outbound

This paper cites an unresolved cited work.

From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models Unresolved cited work

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T23:27:05.423798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b44c7f7c-bb97-47c5-bb35-556d4213ae0b · outbound

This paper cites Creating superpositions that correspond to efficiently integrable probability distributions.

From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models Creating superpositions that correspond to efficiently integrable probability distributions

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T23:27:05.547450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b8b42be-6fe7-4c7d-959a-283a151ef8f0 · outbound

This paper cites Quantum Support Vector Machine for Big Data Classification.

From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models Quantum Support Vector Machine for Big Data Classification

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T23:27:05.623170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed0e57a3-a606-4229-a280-54eda9843daa · outbound

This paper cites A Quantum-Inspired Version of the Nearest Mean Classifier.

From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models A Quantum-Inspired Version of the Nearest Mean Classifier

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T23:27:05.677817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3fcf968d-a970-4015-bff4-fe5039d03703 · outbound

This paper cites An efficient geometric approach to quantum-inspired classifi- cations.

From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models An efficient geometric approach to quantum-inspired classifi- cations

Reference 18

Resolution: verified exact; doi, observed 2026-08-01T23:28:28.379729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8cf0ecf7-ec12-4092-abf6-0cc958b22143 · outbound

This paper cites Quantum-Inspired Applications for Classification Problems.

From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models Quantum-Inspired Applications for Classification Problems

Reference 19

Resolution: verified exact; doi, observed 2026-08-01T23:28:28.254811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation de13e501-c865-46e0-ace7-ceef14b1dfee · outbound

This paper cites Data Re-Uploading for a Universal Quantum Classifier.

From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models Data Re-Uploading for a Universal Quantum Classifier

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T23:27:05.890984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 639ee1dc-7e13-41a5-909e-47ad4ab86769 · outbound

This paper cites What We Can Do with One Qubit in Quantum Machine Learning: Ten Classical Machine Learning Problems That Can Be Solved with a Single Qubit.

From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models What We Can Do with One Qubit in Quantum Machine Learning: Ten Classical Machine Learning Problems That Can Be Solved with a Single Qubit

Reference 21

Resolution: verified exact; doi, observed 2026-08-01T23:28:28.132113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b6a343d1-6e27-48e6-8a2d-fd0248162499 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-01T23:27:05.125657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e40c53f2-fab6-430e-beb5-25d249a199c0 · outbound

This paper cites The Power Of Simplicity: Why Simple Linear Models Outperform Complex Machine Learning Techniques -- Case Of Breast Cancer Diagnosis.

From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models The Power Of Simplicity: Why Simple Linear Models Outperform Complex Machine Learning Techniques -- Case Of Breast Cancer Diagnosis

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-01T23:27:05.489602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.