Paper Citation Record · LEDGER

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning

As of 24 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2507.14919.

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

pith.paper-citation-record.v1
2507.14919 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:49:20.847631Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

39 of 39 outbound references displayed

verified exact3
verified fuzzy13
unresolved20
parse uncertain0
malformed identifier2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6d8c03cc-8b98-4059-ba00-e8473319833a · outbound

This paper cites an unresolved cited work.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-06T15:49:27.550828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8b2f045e-eeb1-4bae-8f14-32db49ad7dff · outbound

This paper cites Algorithms for quantum computation: Discrete logarithms and factoring,.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Algorithms for quantum computation: Discrete logarithms and factoring,

Reference 2

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Source-reported events for the cited work

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Observation c8580b92-f4a4-4326-8ca3-a55dabe7c270 · outbound

This paper cites Quantum algorithm for solving linear systems of equations,.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Quantum algorithm for solving linear systems of equations,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:27.210625Z

Source-reported events for the cited work

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Observation e2c29afa-99b0-4a8f-91d8-602a2b0f5f29 · outbound

This paper cites A rigorous and robust quantum speed-up in supervised machine learning,.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning A rigorous and robust quantum speed-up in supervised machine learning,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:26.946974Z

Source-reported events for the cited work

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Observation 97f1d1ba-1e69-4df1-b102-ec407245dce6 · outbound

This paper cites Supervised quantum machine learning models are kernel methods.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Supervised quantum machine learning models are kernel methods

Reference 5

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Source-reported events for the cited work

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Observation c1ecd917-00b5-4db7-9ddc-548a8ef1f05e · outbound

This paper cites Quantum machine learning in feature Hilbert spaces.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Quantum machine learning in feature Hilbert spaces

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T15:49:16.521078Z

Source-reported events for the cited work

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Observation 4534a4d3-ea4d-481a-aa9a-8141821d070a · outbound

This paper cites Quantum Gaussian Process Regression for Bayesian Optimization,.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Quantum Gaussian Process Regression for Bayesian Optimization,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:26.695340Z

Source-reported events for the cited work

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Observation f645ac3b-49e7-41d0-8ee1-a7124f011ff7 · outbound

This paper cites Bayesian Quantum Neural Networks,.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Bayesian Quantum Neural Networks,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T15:49:16.811864Z

Source-reported events for the cited work

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Observation 2ba3bc26-df94-45bf-b148-241316f607bd · outbound

This paper cites Quantum ensembles of quantum classifiers.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Quantum ensembles of quantum classifiers

Reference 9

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Observation 70ba91b9-1ed1-40e6-9904-f39d6dfd3a89 · outbound

This paper cites Bayesian Deep Learning on a Quantum Computer.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Bayesian Deep Learning on a Quantum Computer

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:49:22.350300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ac4ba637-1808-4d08-87e6-1ed6df8ac17e · outbound

This paper cites Uncertainty in Deep Learning,.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Uncertainty in Deep Learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:26.456320Z

Source-reported events for the cited work

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Observation d7333c0e-42d7-43ae-b7e5-2a317df217e7 · outbound

This paper cites an unresolved cited work.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T15:49:17.386580Z

Source-reported events for the cited work

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Observation c5165dce-155d-4a6e-96f0-e3b1ced39ed8 · outbound

This paper cites Uncertainty Quantification in Ma- chine Learning for Engineering Design and Health Prognostics: A Tutorial,.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Uncertainty Quantification in Ma- chine Learning for Engineering Design and Health Prognostics: A Tutorial,

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cb78b79b-0f15-4f30-b3b0-09e166858f7e · outbound

This paper cites Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:49:21.657579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 594fc0a8-f99e-45cb-9855-58cd373a72ec · outbound

This paper cites Over- fitting in quantum machine learning and entangling dropout,.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Over- fitting in quantum machine learning and entangling dropout,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:26.184240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 23afc506-16ac-4453-9a24-2bcaf3c935fd · outbound

This paper cites A General Approach to Dropout in Quantum Neural Net- works,.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning A General Approach to Dropout in Quantum Neural Net- works,

Reference 16

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Source-reported events for the cited work

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Observation 3a372dff-3e47-4dcd-a6fb-7c5859d7db0b · outbound

This paper cites Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 17

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Source-reported events for the cited work

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Observation d3b49ae9-9639-4770-934b-4c78c9725585 · outbound

This paper cites Polson, V.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Polson, V

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:25.532928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0ccd7c9d-d696-4bc7-b1db-a7c3e3a67d53 · outbound

This paper cites Quan- tum assisted Gaussian process regression,.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Quan- tum assisted Gaussian process regression,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:25.272745Z

Source-reported events for the cited work

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Observation 38ff3a61-ef13-4869-981b-d45eb5c49fe0 · outbound

This paper cites An introduction to variational methods for graph- ical models,.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning An introduction to variational methods for graph- ical models,

Reference 20

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 80e83882-64e3-4fd9-92b6-ae943efe7a23 · outbound

This paper cites Blundell, J.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Blundell, J

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:24.740732Z

Source-reported events for the cited work

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Observation a611f8a0-36d6-4f67-ae87-e94d6a343c31 · outbound

This paper cites Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

Reference 22

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Observation 6cc6fb17-5b7a-4db6-abbe-d01cc34c4090 · outbound

This paper cites Gaussian Processes in Ma- chine Learning,.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Gaussian Processes in Ma- chine Learning,

Reference 23

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Observation 992145e7-d2ac-4d81-81fd-a84d7f13b0a7 · outbound

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Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-06T15:49:24.156200Z

Source-reported events for the cited work

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Observation bdf1c53a-73fa-4d94-a81c-36ba0ea4c2d9 · outbound

This paper cites an unresolved cited work.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Unresolved cited work

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c7a7ce6c-c1d6-4f1f-a3c7-ca5bd98b248c · outbound

This paper cites Data re-uploading for a universal quantum classifier.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Data re-uploading for a universal quantum classifier

Reference 26

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Observation 185fe402-5cb6-47c8-ba6b-d2df554045af · outbound

This paper cites The effect of data encoding on the expressive power of variational quantum machine learning models.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning The effect of data encoding on the expressive power of variational quantum machine learning models

Reference 27

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This paper cites Auto-Encoding Varia- tional Bayes,.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Auto-Encoding Varia- tional Bayes,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:23.542626Z

Source-reported events for the cited work

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Observation 1f252f38-67c6-4bce-bc5f-5d558828d7dc · outbound

This paper cites Obtaining Well Calibrated Probabilities Using Bayesian Binning,.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Obtaining Well Calibrated Probabilities Using Bayesian Binning,

Reference 30

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Observation bf358063-5a4e-4b89-9d8b-2662adbeb966 · outbound

This paper cites Accurate Uncertainties for Deep Learning Using Calibrated Regression.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Accurate Uncertainties for Deep Learning Using Calibrated Regression

Reference 31

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This paper cites Adam: A Method for Stochastic Optimization.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Adam: A Method for Stochastic Optimization

Reference 32

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Observation 1267edd7-7a63-4a9d-a52a-340aaa5f963d · outbound

This paper cites Bergholm et al.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Bergholm et al

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:23.280434Z

Source-reported events for the cited work

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Observation f2253dcb-d1a3-402f-b6db-e40832a0682b · outbound

This paper cites Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T15:49:20.490810Z

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Observation a9c22474-f031-4bbe-8faa-2c11428841fb · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T15:49:20.244509Z

Source-reported events for the cited work

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Observation 762e0792-299a-4286-999d-9c7bf8421a8c · outbound

This paper cites Variational Dropout and the Local Reparameterization Trick.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Variational Dropout and the Local Reparameterization Trick

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T15:49:20.847631Z

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Observation b6a3cccc-d76f-4160-9419-d7687cf8ec5b · outbound

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Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Unresolved cited work

Reference 38

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Source-reported events for the cited work

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Observation 552d69f6-9e03-4ca3-acdd-68652e876f2d · outbound

This paper cites Weight Uncertainty in Neural Networks.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Weight Uncertainty in Neural Networks

Reference 2015

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Source-reported events for the cited work

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Observation ffacf80b-a7dd-4768-b071-4602d93beb69 · outbound

This paper cites Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles.

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-06T15:49:18.953268Z

Source-reported events for the cited work

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Observation 5cdce150-c82f-4f88-bb00-61924f09a4f8 · outbound

This paper cites 00403 [cs, stat].

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning 00403 [cs, stat]

Reference 2022

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

No inbound Pith citation observations are available.