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Paper Citation Record · LEDGER

$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning

As of 8 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2607.11701.

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

pith.paper-citation-record.v1
2607.11701 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T03:46:46.026308Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

16 of 16 outbound references displayed

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  • unresolved16
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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8bb03488-bf05-497e-b152-554ebf5595af · outbound

This paper cites Drug design by machine learning: Support vector machines for pharmaceutical data analysis,.

$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning Drug design by machine learning: Support vector machines for pharmaceutical data analysis,

Reference 1

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source=pdf_text observed=2026-07-14T03:46:46.026308Z digest=sha256:f35c69a6d99dff4bf1ed6fae0397359becf957ebc3099b3510161af47255a8ab

Observation caac28fd-e397-44fd-b32f-6eec490b6e87 · outbound

This paper cites Q 2SAR: A Quantum Multi- ple Kernel Learning Approach for Drug Discovery,.

$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning Q 2SAR: A Quantum Multi- ple Kernel Learning Approach for Drug Discovery,

Reference 2

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source=pdf_text observed=2026-07-14T03:46:46.026308Z digest=sha256:a768839f9d2618b82f981785c31d710d8be17d1321cacda074a248740c7f6ada

Observation 631e284d-c49d-4b70-b279-75b8636eff21 · outbound

This paper cites Quantum support vector machine for big data classification,.

$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning Quantum support vector machine for big data classification,

Reference 3

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source=pdf_text observed=2026-07-14T03:46:46.026308Z digest=sha256:a113b3b8d4a61b3c32f2ffdd55ff77bfa668436060e3562c7da6d0b7d14b72f3

Observation dd35ffbd-eb81-423f-af9b-5d3de7bec527 · outbound

This paper cites Power of data in quantum machine learning,.

$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning Power of data in quantum machine learning,

Reference 4

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source=pdf_text observed=2026-07-14T03:46:46.026308Z digest=sha256:3237673e6b16a4f1cb4f3edc4b6150bbcb1b1950c8fd34dbab3f2a3f5f9e6a46

Observation 38a1c6db-687f-4bc3-8365-0bde9be27cc7 · outbound

This paper cites Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning,.

$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning,

Reference 5

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source=pdf_text observed=2026-07-14T03:46:46.026308Z digest=sha256:5d2ba5d8a287ab75a53a849358b7bd18413872bb32babbcb75bcd6905aed8646

Observation dab947af-66ee-4dd9-b0b6-d57bd95dfd0c · outbound

This paper cites Quantum Multiple Kernel Learning.

$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning Quantum Multiple Kernel Learning

Reference 6

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source=pdf_text observed=2026-07-14T03:46:46.026308Z digest=sha256:cb4d56009bc65ccd897032a4948b54be241493ed36bf1d28ad0e0b969fbfa273

Observation 53a95731-7bd9-4bba-b98d-8e2c607484cf · outbound

This paper cites Gaultonet al.,The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods, Nucleic Acids Research, 2023.

$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning Gaultonet al.,The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods, Nucleic Acids Research, 2023

Reference 7

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source=pdf_text observed=2026-07-14T03:46:46.026308Z digest=sha256:ce15432288d68f437e21322dd538c27c4399dea066eb3b8eeea77b49bf342385

Observation 335d6a08-95f2-4fc8-8b5c-5c27912256ae · outbound

This paper cites Fast Expectation Value Calculation Speedup of Quantum Approximate Optimization Algorithm: HoLCUs QAOA.

$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning Fast Expectation Value Calculation Speedup of Quantum Approximate Optimization Algorithm: HoLCUs QAOA

Reference 8

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source=pdf_text observed=2026-07-14T03:46:46.026308Z digest=sha256:7534bd5eb49a833b8b99c61a89603fe3728436548a06a53c9aaf906927ee118d

Observation 94617f2d-53bb-4949-bb36-1229597a0c45 · outbound

This paper cites Data Complexity: a threshold between Classical and Quantum Machine Learning - Part I,.

$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning Data Complexity: a threshold between Classical and Quantum Machine Learning - Part I,

Reference 9

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source=pdf_text observed=2026-07-14T03:46:46.026308Z digest=sha256:80f64cd2fcc20454495a1276b48388f02d543810d09be64487e28a5428cc0e3a

Observation e1a770fe-ec39-436d-8632-a75a10ccc920 · outbound

This paper cites Supervised learning with quantum- enhanced feature spaces,.

$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning Supervised learning with quantum- enhanced feature spaces,

Reference 10

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source=pdf_text observed=2026-07-14T03:46:46.026308Z digest=sha256:39615e940711fb6abd3463bfbe5a04edcf86e05340344be833eb5c07da538e71

Observation f00987b5-993b-4f3f-b90a-ba2be4b53211 · outbound

This paper cites Quantum machine learning in feature Hilbert spaces,.

$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning Quantum machine learning in feature Hilbert spaces,

Reference 11

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source=pdf_text observed=2026-07-14T03:46:46.026308Z digest=sha256:81d12ce77d6404bfa1e59ea436423a38f0ddac9502958df4fc67c92e4f9a4b70

Observation 88f47ac1-4d9a-4b98-b7d1-9d652996e2d1 · outbound

This paper cites Exponential concentration in quantum kernel methods,.

$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning Exponential concentration in quantum kernel methods,

Reference 12

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source=pdf_text observed=2026-07-14T03:46:46.026308Z digest=sha256:c38b2dc285beea95759123957a777a7c7bac85db24ade6288702e70c12bd2055

Observation 9dc803b8-7d56-4c94-84bb-0ad701ecb559 · outbound

This paper cites Enhancing Drug Discovery: Quantum Machine Learning for QSAR Prediction with Incomplete Data.

$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning Enhancing Drug Discovery: Quantum Machine Learning for QSAR Prediction with Incomplete Data

Reference 13

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source=pdf_text observed=2026-07-14T03:46:46.026308Z digest=sha256:ac8d7ad3e3b7f0071a57f4d2dc4d241dcc626b855b6062fe7b2428ea25f9988b

Observation 20accb01-a247-48b0-bb9d-68b88757ce12 · outbound

This paper cites Guidance for good practice in the application of machine learning in development of toxicological quantitative structure-activity relationships (QSARs),.

$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning Guidance for good practice in the application of machine learning in development of toxicological quantitative structure-activity relationships (QSARs),

Reference 14

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source=pdf_text observed=2026-07-14T03:46:46.026308Z digest=sha256:ee3620c151412fadac23a665eb15106dbdaebaf7345152d8ff133516cb5d8852

Observation 8c13de1d-c02b-4347-8d4d-8b096e849716 · outbound

This paper cites Pesticide effect on earthworm lethality via interpretable machine learning,.

$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning Pesticide effect on earthworm lethality via interpretable machine learning,

Reference 15

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source=pdf_text observed=2026-07-14T03:46:46.026308Z digest=sha256:b5dcb51688c72a9afe851b88394cd0ec5adf4055b6ac9458c147e94e2eb2c48c

Observation f78cd21b-83fe-44fa-833c-3d1ff1276aab · outbound

This paper cites Logistic classification models for pH- permeability profile: Predicting permeability classes for the biopharma- ceutical classification system,.

$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning Logistic classification models for pH- permeability profile: Predicting permeability classes for the biopharma- ceutical classification system,

Reference 16

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source=pdf_text observed=2026-07-14T03:46:46.026308Z digest=sha256:c100b8c905768d5f26d9958ea27f288c796c96654a1c27729e2b730ae6fe883b

Pith citing papers

No inbound Pith citation observations are available.