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Paper Citation Record · LEDGER

Supervised quantum machine learning models are kernel methods

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 59 inbound Pith citation observations for arXiv:2101.11020.

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

pith.paper-citation-record.v1
2101.11020 v2

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measured 59 of 59 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 59 of 59 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:30:12.559177Z

measured 1 of 1 external citation measurements

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

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

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External citation measurements

172
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9602bc51-9837-4210-a6a4-817d13a5c851 · inbound

Quantum Computing for Energy Management: A Semi Non-Technical Guide for Practitioners cites this paper.

Quantum Computing for Energy Management: A Semi Non-Technical Guide for Practitioners Supervised quantum machine learning models are kernel methods

Reference 83

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source=pdf_text observed=2026-08-12T20:18:06.175115Z digest=sha256:e5df5ef7226c7443305c1080633a1159cbf6b488446c963674fa364dfb4395ce

Observation dec6917e-4c99-4158-87aa-6444260421d5 · inbound

Addressing the Readout Problem in Quantum Differential Equation Algorithms with Quantum Scientific Machine Learning cites this paper.

Addressing the Readout Problem in Quantum Differential Equation Algorithms with Quantum Scientific Machine Learning Supervised quantum machine learning models are kernel methods

Reference 64

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source=pdf_text observed=2026-08-12T15:25:30.543325Z digest=sha256:4d2ba1ea0658d20af4ace757913eb77772408d2be06d4a8cdc552c152fce9d8e

Observation 4462d550-db07-4c83-893d-de822b48c764 · inbound

Unsupervised Quantum Anomaly Detection on Noisy Quantum Processors cites this paper.

Unsupervised Quantum Anomaly Detection on Noisy Quantum Processors Supervised quantum machine learning models are kernel methods

Reference 18

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source=pdf_text observed=2026-08-12T12:46:23.478371Z digest=sha256:14dca11175a185a4a41b64bb8b411098d4dea9cbcaa206749c2ef3fac2f78a79

Observation 68450c42-7289-4b27-af0f-da551bad5bbd · inbound

The role of data-induced randomness in quantum machine learning classification tasks cites this paper.

The role of data-induced randomness in quantum machine learning classification tasks Supervised quantum machine learning models are kernel methods

Reference 11

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source=pdf_text observed=2026-08-12T10:35:11.901754Z digest=sha256:7f955396d61ff4d7153ad26db68a57ba33f4706a8ee362b195d28b7175c06499

Observation 9de0f28d-3aab-4e6f-93e2-6e033594f7e1 · inbound

Optimizing Quantum Embedding using Genetic Algorithm for QML Applications cites this paper.

Optimizing Quantum Embedding using Genetic Algorithm for QML Applications Supervised quantum machine learning models are kernel methods

Reference 2

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source=pdf_text observed=2026-08-12T05:36:58.760680Z digest=sha256:9e008b031b5c6e4f3524913d25f398261a324a6b8c4a4f7439c9b91d7444acdd

Observation faf2d36e-b176-47ee-bb68-a2fb8d59205b · inbound

Robust Quantum Reservoir Computing for Molecular Property Prediction cites this paper.

Robust Quantum Reservoir Computing for Molecular Property Prediction Supervised quantum machine learning models are kernel methods

Reference 16

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source=pdf_text observed=2026-08-11T19:22:34.673253Z digest=sha256:909c5f965b7ea8433e210a697ceb043f3a776820640da3c1e11fb133732f486b

Observation 26952104-2dbc-4719-8f53-eb9068187c76 · inbound

Opportunities and limitations of explaining quantum machine learning cites this paper.

Opportunities and limitations of explaining quantum machine learning Supervised quantum machine learning models are kernel methods

Reference 92

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source=pdf_text observed=2026-08-11T12:02:09.238053Z digest=sha256:4c19e5db3c19a8e3f2d8703daa447ca25efabaf9a854cb30d12f3f670ff8096e

Observation 3316081e-8025-4a5e-b508-14d950270990 · inbound

Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers cites this paper.

Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers Supervised quantum machine learning models are kernel methods

Reference 148

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source=arxiv_source observed=2026-08-09T16:30:40.041769Z digest=sha256:73cbaccd2b9f224e552f932a30d040ba65059fe5a4f8af3a48717c007c7aa1fd

Observation 5ff4c36c-e9ad-4679-a53c-d0ba929040ce · inbound

A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networks cites this paper.

A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networks Supervised quantum machine learning models are kernel methods

Reference 12

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source=pdf_text observed=2026-08-16T12:30:12.559177Z digest=sha256:4f391b3732ead78d8c604b2a1a58bf3439d62453d86df63368b2bd246056b211

Observation 1a5c87df-263c-4fe6-adeb-2a6be1ac84eb · inbound

On the Generalization of Adversarially Trained Quantum Classifiers cites this paper.

On the Generalization of Adversarially Trained Quantum Classifiers Supervised quantum machine learning models are kernel methods

Reference 55

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source=pdf_text observed=2026-08-16T10:43:28.832112Z digest=sha256:18b920bf4163707da6e76a79fa08c2bb5546181834a1ddd003b3ecdd05892404

Observation e428eb52-78e3-4f44-8603-a1f05cd4db2a · inbound

Enhancing the Dynamic Range of Quantum Sensing via Quantum Circuit Learning cites this paper.

Enhancing the Dynamic Range of Quantum Sensing via Quantum Circuit Learning Supervised quantum machine learning models are kernel methods

Reference 27

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source=pdf_text observed=2026-08-15T23:22:10.098537Z digest=sha256:c7c0ca2f4194c4d46d48808b3ff077946c050f79bf3ff27e9f831930510a0b23

Observation c3975f45-1c01-498d-aca6-da5e2416c371 · inbound

Quantum Surrogate-Driven Image Classifier: A Gradient-Free Approach to Avoid Barren Plateaus cites this paper.

Quantum Surrogate-Driven Image Classifier: A Gradient-Free Approach to Avoid Barren Plateaus Supervised quantum machine learning models are kernel methods

Reference 11

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source=pdf_text observed=2026-08-15T23:14:31.362452Z digest=sha256:f53cefb1b89fbe8de80c06565e75190be5bbb696788c81c009ad2dcc64de4108

Observation 9b22ed31-c8cb-4548-911e-dbaa14c96282 · inbound

Quantum Multi-view Kernel Learning with Local Information cites this paper.

Quantum Multi-view Kernel Learning with Local Information Supervised quantum machine learning models are kernel methods

Reference 24

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source=pdf_text observed=2026-08-07T15:04:01.000753Z digest=sha256:0c238421998239701d3555c0e206a3302dca4a1350c1c65cd356efa01b26724f

Observation f37c2493-21d7-42c1-8805-510ddb1e530b · inbound

Quantum Machine Learning for Predicting Anastomotic Leak: A Clinical Study cites this paper.

Quantum Machine Learning for Predicting Anastomotic Leak: A Clinical Study Supervised quantum machine learning models are kernel methods

Reference 14

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source=pdf_text observed=2026-08-07T11:40:11.847916Z digest=sha256:503a21156794cf5c05989a0b686ca2453e14f4afaa31bd40a8973b4ef9d42196

Observation d836a390-5081-4afc-b63a-c70c864b0f9d · inbound

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential cites this paper.

Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential Supervised quantum machine learning models are kernel methods

Reference 76

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source=arxiv_source observed=2026-08-07T11:03:04.919706Z digest=sha256:30d89972523ef17c5fa13da43eb88f4ea3761d471eccbfc47e4cf67d58735b30

Observation 47df7d69-14d9-4bf2-967c-d57bf3a30eb0 · inbound

Genetic Transformer-Assisted Quantum Neural Networks for Optimal Circuit Design cites this paper.

Genetic Transformer-Assisted Quantum Neural Networks for Optimal Circuit Design Supervised quantum machine learning models are kernel methods

Reference 31

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source=pdf_text observed=2026-08-07T05:00:33.992454Z digest=sha256:59b6e5d56c434b4523a508ce409058c001b6df5a005e61f1ea310d27eee6fe91

Observation 5e939f62-807f-4f5e-98c7-69a1221a18c1 · inbound

Quantum Recurrent Embedding Neural Network cites this paper.

Quantum Recurrent Embedding Neural Network Supervised quantum machine learning models are kernel methods

Reference 58

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source=pdf_text observed=2026-08-07T00:40:08.993601Z digest=sha256:3bfdfd5bd3ad3c662f393b7988c913f2307ff02fd2fdabba738c42f7db5b42d7

Observation e52093bd-a06c-47f2-8883-876b5c105475 · inbound

Quantum Spectral Clustering: Comparing Parameterized and Neuromorphic Quantum Kernels cites this paper.

Quantum Spectral Clustering: Comparing Parameterized and Neuromorphic Quantum Kernels Supervised quantum machine learning models are kernel methods

Reference 30

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arxiv_id, observed 2026-05-19T05:57:08.257573Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-19T05:53:50.320446Z digest=sha256:f665d760fe78f73211fa074d3127aaea093820b4241c25a0f19f249d31f6a580

Observation 0d2e1f8a-c11c-44ce-a267-c7c7cb7b84aa · inbound

Enhanced image classification via hybridizing quantum dynamics with classical neural networks cites this paper.

Enhanced image classification via hybridizing quantum dynamics with classical neural networks Supervised quantum machine learning models are kernel methods

Reference 83

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source=pdf_text observed=2026-08-06T16:27:40.724598Z digest=sha256:21e179c0ac5e52e07eb10f5ac0d3fcce659475c1b574103edd624b20421020eb

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

Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning cites this paper.

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=pdf_text observed=2026-08-06T15:49:16.319927Z digest=sha256:e7d6f3c36f2018117f710a622e78b85dbcc6655e117a41de074ec40a0913d917

Observation 225d4f29-afe9-4412-8930-e43a5344fb37 · inbound

Pitfalls when tackling the exponential concentration of parameterized quantum models cites this paper.

Pitfalls when tackling the exponential concentration of parameterized quantum models Supervised quantum machine learning models are kernel methods

Reference 11

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source=pdf_text observed=2026-08-06T12:11:56.740005Z digest=sha256:0a530f6e6c8a978940f9fec32cda25cf4836ad7b75bf1d709150afaca0de004a

Observation dcace0c5-e2de-4879-8d72-778be93df5dd · inbound

Concentration-Free Quantum Kernel Learning in the Rydberg Blockade cites this paper.

Concentration-Free Quantum Kernel Learning in the Rydberg Blockade Supervised quantum machine learning models are kernel methods

Reference 7

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source=arxiv_source observed=2026-08-05T20:14:59.068595Z digest=sha256:1088adfe89f24614299e0b13c5ce2158794944bdecf165003a7fb2431e9b6240

Observation 3855df1b-4915-4a9b-a3a1-ebcd65ceca62 · inbound

Quantum Relational Knowledge Distillation cites this paper.

Quantum Relational Knowledge Distillation Supervised quantum machine learning models are kernel methods

Reference 19

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source=arxiv_source observed=2026-08-05T19:15:31.503396Z digest=sha256:075a193cc30a65f205efd3e142d68372ccdb0e98776891d5a388e4acc3e70061

Observation e0155b98-e5c4-444d-bea8-3ac4a714d961 · inbound

Is data-efficient learning feasible with quantum models? cites this paper.

Is data-efficient learning feasible with quantum models? Supervised quantum machine learning models are kernel methods

Reference 7

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source=pdf_text observed=2026-08-05T15:53:38.850743Z digest=sha256:9e715743fbf58faec43d4b68e8dd37eec907e13a2627c2ec8530f18f62bb03d4

Observation ad6de43f-b7e1-4242-acd7-696e265c4633 · inbound

Quantum Circuits for Quantum Convolutions: A Quantum Convolutional Autoencoder cites this paper.

Quantum Circuits for Quantum Convolutions: A Quantum Convolutional Autoencoder Supervised quantum machine learning models are kernel methods

Reference 19

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source=pdf_text observed=2026-08-05T13:26:38.381525Z digest=sha256:68ab33c26b1ae5e0382ff34a87514929f31af2ab3edabc3049fd31a4d0762928

Observation f1680a90-95d6-4e49-95bc-dbf3a610b48b · inbound

From Membership-Privacy Leakage to Quantum Machine Unlearning cites this paper.

From Membership-Privacy Leakage to Quantum Machine Unlearning Supervised quantum machine learning models are kernel methods

Reference 42

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arxiv_id, observed 2026-05-18T18:11:42.851398Z

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source=pdf_text observed=2026-05-18T18:08:00.127348Z digest=sha256:4fea39c0bbf98dbba8504375114694be1540623fc3689d796ba81200256450b8

Observation 6bee7ab5-f36e-40b8-af94-5b0bfc86b947 · inbound

Quantum kernel and HHL-based support vector machines for multi-class classification cites this paper.

Quantum kernel and HHL-based support vector machines for multi-class classification Supervised quantum machine learning models are kernel methods

Reference 15

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source=pdf_text observed=2026-08-04T18:10:57.284732Z digest=sha256:5fcbd2c34d1f23a0c2496ea20be345b29e6eaf7d37bc0ec1823d13d4250e1998

Observation 7b359317-caa5-495e-b7c7-1c620028fbf1 · inbound

Toward selective quantum advantage in hadronic tomography:explicit cases from Compton form factors, GPDs, TMDs, and GTMDs cites this paper.

Toward selective quantum advantage in hadronic tomography:explicit cases from Compton form factors, GPDs, TMDs, and GTMDs Supervised quantum machine learning models are kernel methods

Reference 27

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source=pdf_text observed=2026-05-10T16:46:32.403391Z digest=sha256:7807db6b42d10dc95b92c3260365a88b708608bb48fdcae72050a54e356e37be

Observation 7909424b-e798-4b68-89dd-6b629495a0f4 · inbound

Answering Counting Queries with Differential Privacy on a Quantum Computer cites this paper.

Answering Counting Queries with Differential Privacy on a Quantum Computer Supervised quantum machine learning models are kernel methods

Reference 21

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arxiv_id, observed 2026-05-11T08:56:01.291248Z

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source=pdf_text observed=2026-05-10T16:25:06.701330Z digest=sha256:dc641469358e3f0385eb0a23e5bab4c19bcbe80279bd845e2aa3d95e0f744188

Observation 1fcfc208-ab53-4e62-910f-a5d9f46445d0 · inbound

Feature-level analysis and adversarial transfer in rotationally equivariant quantum machine learning cites this paper.

Feature-level analysis and adversarial transfer in rotationally equivariant quantum machine learning Supervised quantum machine learning models are kernel methods

Reference 29

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arxiv_id, observed 2026-05-10T10:44:38.230126Z

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source=pdf_text observed=2026-05-10T10:30:52.636240Z digest=sha256:dc08af3d2660fec6ced75ba963ddca972d5acda7c078aaf15d98fe5ac6fa0e36

Observation 3bd523bf-7b31-4f17-b0dc-cb65a22fb372 · inbound

Double Descent in Quantum Kernel Ridge Regression cites this paper.

Double Descent in Quantum Kernel Ridge Regression Supervised quantum machine learning models are kernel methods

Reference 48

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arxiv_id, observed 2026-05-10T06:46:37.406865Z

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source=pdf_text observed=2026-05-10T06:42:30.100063Z digest=sha256:0310a0daaae971a1d0f21ea35c057d9acdd480c76eecf54afdbd18c2fa45dd5d

Observation fce68469-9d10-4d0a-972b-4c2878f5733b · inbound

Towards Automated Selection of Quantum Encoding Circuits via Meta-Learning cites this paper.

Towards Automated Selection of Quantum Encoding Circuits via Meta-Learning Supervised quantum machine learning models are kernel methods

Reference 16

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arxiv_id, observed 2026-05-10T03:24:14.816208Z

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source=pdf_text observed=2026-05-10T03:14:30.758628Z digest=sha256:46453f2919a826556c697cb2a6c0575a50c0a9640b12c2773c4c3b143fd0d065

Observation c5f8fd70-95d6-4922-9ce5-b1f8b6caf588 · inbound

Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classification cites this paper.

Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classification Supervised quantum machine learning models are kernel methods

Reference 6

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arxiv_id, observed 2026-05-12T08:51:25.485410Z

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source=pdf_text observed=2026-05-07T13:35:03.831797Z digest=sha256:609059db5eadd3d238f33fc63a792fbd2f8531d13c74505fdd3fdd1872d95306

Observation eef03bdc-aba5-48b8-ae08-6f3637caad03 · inbound

Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classification cites this paper.

Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classification Supervised quantum machine learning models are kernel methods

Reference 6

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source=pdf_text observed=2026-08-02T15:18:49.420161Z digest=sha256:acb6f282a92adf01cbbc040b73234a0e44ad6c14608281ecfe0a166f9bb428e2

Observation b18bcdcb-4819-4e7a-8067-8c200ea2996c · inbound

Measuring Accuracy and Energy-to-Solution of Quantum Fine-Tuning of Foundational AI Models cites this paper.

Measuring Accuracy and Energy-to-Solution of Quantum Fine-Tuning of Foundational AI Models Supervised quantum machine learning models are kernel methods

Reference 27

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arxiv_id, observed 2026-05-09T06:40:40.427332Z

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source=pdf_text observed=2026-05-08T18:14:03.049005Z digest=sha256:0369fa663b7a44c0974e5f9da7db68eb1361550f61ea1bef185c4ecbf5e9e1c5

Observation a9928c3a-60f1-4818-a34f-e2a52688af92 · inbound

Scalable Quantum Reservoir Computing over Distributed Quantum Architectures cites this paper.

Scalable Quantum Reservoir Computing over Distributed Quantum Architectures Supervised quantum machine learning models are kernel methods

Reference 30

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arxiv_id, observed 2026-05-11T18:16:10.457935Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-08T16:23:20.803906Z digest=sha256:f4c10b86d83d0b1250dc2ac8404c1da51b0acb02903d3f7c9e25877f9cf84079

Observation 8e8a624a-9a63-4085-8d1b-fd3d2a012290 · inbound

Quantum Kernels for Parity-Structured Classification: A Hybrid Pipeline cites this paper.

Quantum Kernels for Parity-Structured Classification: A Hybrid Pipeline Supervised quantum machine learning models are kernel methods

Reference 6

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arxiv_id, observed 2026-05-11T19:31:09.998589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-08T11:43:25.569269Z digest=sha256:08ed00300cb249038e7d88ac796d75cff02623a0ae76c0e68ebad93c615ea393

Observation ff8a950f-97e2-442d-b0ae-83813e01e53c · inbound

A Quantum Inspired Variational Kernel and Explainable AI Framework for Cross Region Solar and Wind Energy Forecasting cites this paper.

A Quantum Inspired Variational Kernel and Explainable AI Framework for Cross Region Solar and Wind Energy Forecasting Supervised quantum machine learning models are kernel methods

Reference 19

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T01:56:15.134374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-12T01:54:01.908035Z digest=sha256:f3030f617977e513a1fcc275b18399538ca40731437df06d6a314f3cd65701ca

Observation 48214d08-7869-4d01-a16e-d8b69e75cc6b · inbound

Controllable Quantum Memory Capacity in Quantum Reservoir Networks with Tunable partial-SWAPs cites this paper.

Controllable Quantum Memory Capacity in Quantum Reservoir Networks with Tunable partial-SWAPs Supervised quantum machine learning models are kernel methods

Reference 20

Resolution
malformed identifier
arxiv_id, observed 2026-05-14T21:58:03.728373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-14T21:54:21.118152Z digest=sha256:9da15c7c545c8fc2a5b861fced7affdf2d8c496892ee4d51a16b56ddffcac437

Observation 3e094582-cfa2-450f-8536-ac51dd4bfa4e · inbound

Controllable Quantum Memory Capacity in Quantum Reservoir Networks with Tunable partial-SWAPs cites this paper.

Controllable Quantum Memory Capacity in Quantum Reservoir Networks with Tunable partial-SWAPs Supervised quantum machine learning models are kernel methods

Reference 20

Resolution
metadata mismatch
arxiv_id, observed 2026-05-19T17:27:41.428281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-19T17:23:39.552129Z digest=sha256:8e5bf70a936c4018264183c47752bfd33bbcc8e103cd97969b5368a7e77e9ac2

Observation e135af0e-7bcf-46ad-8c72-9f1db9161bc6 · inbound

Controllable Quantum Memory Capacity in Quantum Reservoir Networks with Tunable partial-SWAPs cites this paper.

Controllable Quantum Memory Capacity in Quantum Reservoir Networks with Tunable partial-SWAPs Supervised quantum machine learning models are kernel methods

Reference 20

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T14:15:47.488653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-30T22:06:57.435443Z digest=sha256:8abdc2596fca21dec18f3476c5b62a6161ffde09ab6e5c755f7f1bda620fb813

Observation 6057e702-7a08-411b-a341-d26ebefeb192 · inbound

AQKA: Active Quantum Kernel Acquisition Under a Shot Budget cites this paper.

AQKA: Active Quantum Kernel Acquisition Under a Shot Budget Supervised quantum machine learning models are kernel methods

Reference 2

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T21:25:04.303778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-30T21:22:48.644872Z digest=sha256:eda87111f79ccac3e0cdcbe3f4ca1bd7c0c7111640dd77a4388ae0955cc4576e

Observation afaf7203-017f-45af-9e4d-bf23e88e765d · inbound

AQKA: Active Quantum Kernel Acquisition Under a Shot Budget cites this paper.

AQKA: Active Quantum Kernel Acquisition Under a Shot Budget Supervised quantum machine learning models are kernel methods

Reference 2018

Resolution
unresolved
no resolver link, observed 2026-08-02T14:05:17.863369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T14:05:17.863369Z digest=sha256:2d11bd1e426cafc4d973ccf3050dcce2d970e0b3f72590e80051a686dcec1ebe

Observation 07efb795-c6dc-4f50-bf4c-3cb6d47177ca · inbound

Off-line quantum-advantage feature extraction for industrial production cites this paper.

Off-line quantum-advantage feature extraction for industrial production Supervised quantum machine learning models are kernel methods

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-20T05:53:04.677614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-20T05:52:07.212015Z digest=sha256:a421aa270fac06cf9a0c4d252b88452019a2907425d6c7f7b4e76ffea7eaadd2

Observation 657a2821-8205-4e58-b160-fc96fa078cce · inbound

Benchmarking a machine-learning differential equations solver on a neutral-atom logical processor cites this paper.

Benchmarking a machine-learning differential equations solver on a neutral-atom logical processor Supervised quantum machine learning models are kernel methods

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-21T04:39:35.008511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-21T04:39:16.463390Z digest=sha256:643141644e5116bca7a1ec0a320ff0b76fe36260637b70b9af7f0826250c8430

Observation 74d4fa1c-6d52-4c0b-a112-c486dbe5658e · inbound

Quantum Parameterized Self-Attention Network for Image Classification cites this paper.

Quantum Parameterized Self-Attention Network for Image Classification Supervised quantum machine learning models are kernel methods

Reference 19

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T22:03:59.980861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T22:02:43.573430Z digest=sha256:655ffeb34d097c7168aba5ae706469d318834c6267d6ca2eb4562c36dfa88271

Observation 1ab86828-941d-47de-950a-c0cc1e7a516c · inbound

Meta-Quantum Ensemble Framework for Robust Network Intrusion Detection cites this paper.

Meta-Quantum Ensemble Framework for Robust Network Intrusion Detection Supervised quantum machine learning models are kernel methods

Reference 5

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T17:13:44.920111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T17:05:05.681726Z digest=sha256:bd48a5cf8ded311cfefd689f1281977c7f8d146735d64e147c602f3a3e6b08be

Observation f8f7ceee-9c0a-473f-9a40-368d879fe677 · inbound

Quantum encodings that preserve persistent homology cites this paper.

Quantum encodings that preserve persistent homology Supervised quantum machine learning models are kernel methods

Reference 85

Resolution
verified exact
arxiv_id, observed 2026-06-29T11:53:24.389341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T11:45:53.392594Z digest=sha256:0ff18f1a7c3a972edf82e4ea1365650d447dfa0d62e9c536e5564ceb6104566e

Observation 4f130ab6-e150-4d39-97f5-40c6b16c0e43 · inbound

Feature Encoding in Quantum Machine Learning: A Survey and Practical Guidelines cites this paper.

Feature Encoding in Quantum Machine Learning: A Survey and Practical Guidelines Supervised quantum machine learning models are kernel methods

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-02T12:26:02.982368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T12:26:02.982368Z digest=sha256:5222c92de2fb3cafaca4aa142981e8fdcb009727704f8f82b386f814ae3a6e90

Observation b194bc6f-ad5d-47c1-84f3-e4e3a37aae8a · inbound

Effective Dimension Governs Generalization in Quantum Kernel Vision Models cites this paper.

Effective Dimension Governs Generalization in Quantum Kernel Vision Models Supervised quantum machine learning models are kernel methods

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-07-04T03:29:29.614922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-06-26T18:04:51.851236Z digest=sha256:6b21c07d759cc40eedeefeaaa8aa06bc9da6a6890312c4608a539b3f0a3a1925

Observation 556e0fd6-c8d4-4ce8-83cf-8f1938def860 · inbound

Quantum Kernels are Spectral Tensor Networks cites this paper.

Quantum Kernels are Spectral Tensor Networks Supervised quantum machine learning models are kernel methods

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-07-04T04:29:34.921527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-26T16:59:38.105865Z digest=sha256:ef1e3ba3f14987f94d04b93dfe7fe22b9795673d1d6a847151e1a3056e7e2560

Observation 7d181f89-d251-43d5-9f35-e50f7d2f1436 · inbound

QBioFusion-QSAR: Morgan-Anchored Quantum Multiple Kernel Learning for Small-Data Ligand Classification cites this paper.

QBioFusion-QSAR: Morgan-Anchored Quantum Multiple Kernel Learning for Small-Data Ligand Classification Supervised quantum machine learning models are kernel methods

Reference 7

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T07:39:39.113584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-26T13:08:47.370996Z digest=sha256:2c0c8272e5f19660ca4d54407f685171a215261722243ea80793b12b69edf917

Observation fb5ce4c7-ca40-4005-9907-ec21ee3d0c5c · inbound

Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification cites this paper.

Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification Supervised quantum machine learning models are kernel methods

Reference 28

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T09:09:43.027792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-26T10:30:01.477176Z digest=sha256:2e534ec23308efdc8ecde51cba656614af38716494cd70744af68d9121fb920c

Observation 58402860-5f5d-4cab-97a2-a0148c339dc1 · inbound

Invariance Audits for Quantum Kernels and Variational Rewinding: A Real-to-Hermitian Taxonomy of Projector, Flag, Anchor, and Density Geometry cites this paper.

Invariance Audits for Quantum Kernels and Variational Rewinding: A Real-to-Hermitian Taxonomy of Projector, Flag, Anchor, and Density Geometry Supervised quantum machine learning models are kernel methods

Reference 10

Resolution
verified exact
local_arxiv, observed 2026-07-10T15:17:21.686198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-07-10T15:15:57.492512Z digest=sha256:f83e088e8094e85d79795669072014bce01d9e9c52962456f62c4afc31e2c2c0

Observation aec94693-af5f-499c-a08d-ecc19d3eb1de · inbound

Learning Topological Quantum Phases from Limited Subsystems cites this paper.

Learning Topological Quantum Phases from Limited Subsystems Supervised quantum machine learning models are kernel methods

Reference 32

Resolution
unresolved
no resolver link, observed 2026-07-14T10:11:26.344146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T10:11:26.344146Z digest=sha256:10ec46ff564abf227ecbf14865aa02c07d9b5ee0cdde2cc2f207cea7af300b28

Observation 1d02f325-a01e-4953-a7b8-75d255093055 · inbound

Quantum Topological Data Encoding cites this paper.

Quantum Topological Data Encoding Supervised quantum machine learning models are kernel methods

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-02T03:38:02.516390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T03:38:02.516390Z digest=sha256:f422317dadd297c54da57db5a7e28a1600ab2140ab5bb3c364f8cab0d0f7f156

Observation a4bb69ac-b331-4451-816c-7da26e100d63 · inbound

A Multiclass Quantum Aligned Centroid Kernel cites this paper.

A Multiclass Quantum Aligned Centroid Kernel Supervised quantum machine learning models are kernel methods

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-01T11:48:19.348484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T11:48:19.348484Z digest=sha256:d53e7c1a2daeaebba2f11f16cd02be3350f4a5161f84e3d531667357161c721d

Observation 9e80a9a1-bca3-4f39-ba7d-c8a0a595a4a6 · inbound

Quantum Kernels and the Cross-Section of Stock Returns: Anatomy of a Vanishing Advantage cites this paper.

Quantum Kernels and the Cross-Section of Stock Returns: Anatomy of a Vanishing Advantage Supervised quantum machine learning models are kernel methods

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-01T10:40:12.750741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T10:40:12.750741Z digest=sha256:868c798617ada5665fdf7ab1daf0a663bc2ae934b1cc63a50bbf4bd2da468a2f

Observation 74f31a3b-530e-40f9-968a-a83a03ba5f93 · inbound

Statevector-Referenced Geometry Survival of a Four-Qubit ZZ Quantum Kernel on IBM Quantum Hardware: A Fixed-Subset Diagnostic Across Three Execution Configurations cites this paper.

Statevector-Referenced Geometry Survival of a Four-Qubit ZZ Quantum Kernel on IBM Quantum Hardware: A Fixed-Subset Diagnostic Across Three Execution Configurations Supervised quantum machine learning models are kernel methods

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-01T10:02:31.110942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T10:02:31.110942Z digest=sha256:2c344153dbfd87746b3c8772a2a69c35c951db8c93a0bf12c8057a4515c940ef