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Paper Citation Record · LEDGER

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation

As of 14 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 1 inbound Pith citation observation for arXiv:2505.20863.

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

pith.paper-citation-record.v1
2505.20863 v3

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:51:37.481164Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-09T14:54:25.961118Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T16:51:05.830919Z

Reference resolution

37 of 37 outbound references displayed

  • verified exact4
  • verified fuzzy14
  • unresolved18
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 69a77b31-21bb-42ec-9a32-0bfea12ebdda · outbound

This paper cites Quantum circuit synthesis with diffusion models,.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Quantum circuit synthesis with diffusion models,

Reference 1

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation f3e038e2-4ec7-4465-927e-813aec444099 · outbound

This paper cites Quantum computing in the nisq era and beyond,.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Quantum computing in the nisq era and beyond,

Reference 2

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source=pdf_text observed=2026-08-07T13:51:33.835543Z digest=sha256:48c86916541b523597c0bfcea76c823297e8db6d7757059cd7eafcde8a542500

Observation 775e859c-4740-4b98-bdd2-d2e4d829cc63 · outbound

This paper cites ´Eliv´agar: Efficient quantum circuit search for classification,.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation ´Eliv´agar: Efficient quantum circuit search for classification,

Reference 3

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source=pdf_text observed=2026-08-07T13:51:34.126160Z digest=sha256:472774a5af50f1a6312f4136858e7179c754974ba582d6d89ca2d16a8337c501

Observation cd99f5f3-8c12-48ad-a68e-fa1202e86271 · outbound

This paper cites Curriculum reinforcement learning for quantum architecture search under hardware errors.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Curriculum reinforcement learning for quantum architecture search under hardware errors

Reference 4

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source=pdf_text observed=2026-08-07T13:51:34.235091Z digest=sha256:369b474bcb459c45cfb65cceb78f319b275ed2c24c31c77a540f4e051b993c9a

Observation 5f467a55-9b0e-4bce-abad-86a9cf09caaf · outbound

This paper cites Quantumnas: Noise-adaptive search for robust quantum circuits,.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Quantumnas: Noise-adaptive search for robust quantum circuits,

Reference 5

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source=pdf_text observed=2026-08-07T13:51:34.379219Z digest=sha256:0a113bca9bf78a4c75d1be491d67fb2da21e252318128c017289a08df5b02666

Observation 3aa89d39-4d5e-4635-8130-3ed6265688e1 · outbound

This paper cites MoG-VQE: Multiobjective genetic variational quantum eigensolver.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation MoG-VQE: Multiobjective genetic variational quantum eigensolver

Reference 6

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source=pdf_text observed=2026-08-07T13:51:34.508744Z digest=sha256:2a9ffae9d07dc11a29910a0d6a42658e2efb7556e7da5e6b7e526701c68915f7

Observation 6da51825-9372-44df-90f4-7e729407deea · outbound

This paper cites GA4QCO: Genetic Algorithm for Quantum Circuit Optimization.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation GA4QCO: Genetic Algorithm for Quantum Circuit Optimization

Reference 7

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source=pdf_text observed=2026-08-07T13:51:34.683330Z digest=sha256:f92efb909c2ec7eb886ecdc59dc49e0817db89f8bb803912960e4c7089c8f6a6

Observation cf19688a-0b30-4093-b3f8-c363b58e9735 · outbound

This paper cites GASP -- A Genetic Algorithm for State Preparation.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation GASP -- A Genetic Algorithm for State Preparation

Reference 8

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source=pdf_text observed=2026-08-07T13:51:34.814383Z digest=sha256:7d557db50f9012b5b8e356c55fac08231fdaddbbfa7d7a10716cad665c58931c

Observation 9dcc42ea-0fd1-411d-8090-35e9d7057547 · outbound

This paper cites Quantum circuit compilation by genetic algorithm for quantum approximate optimization algorithm applied to maxcut problem,.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Quantum circuit compilation by genetic algorithm for quantum approximate optimization algorithm applied to maxcut problem,

Reference 9

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source=pdf_text observed=2026-08-07T13:51:34.947527Z digest=sha256:2dce56ea578d3585b1019cf85a40c893766810454ced03aae50f123383c13f09

Observation 8470e612-dbae-4e7f-a1e2-694d9721e34d · outbound

This paper cites Quantum circuit structure learning,.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Quantum circuit structure learning,

Reference 10

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source=pdf_text observed=2026-08-07T13:51:35.119569Z digest=sha256:9c8e89f2edbb33ff3cbccca8117207b6d7056416cc1a80977fc1beaafab5b9c3

Observation 4d5d93ad-a423-4c19-85cf-aefeb6d9636a · outbound

This paper cites Quantum compiling by deep reinforcement learning,.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Quantum compiling by deep reinforcement learning,

Reference 11

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source=pdf_text observed=2026-08-07T13:51:35.278520Z digest=sha256:b3b5124362619e2be405175275e51f4cbc5699a94a238aa6633be7e32c63ec79

Observation b979d731-5329-420b-977d-611dff7cc9ae · outbound

This paper cites Reinforcement learning for optimization of variational quantum circuit architectures.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Reinforcement learning for optimization of variational quantum circuit architectures

Reference 12

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source=pdf_text observed=2026-08-07T13:51:35.428551Z digest=sha256:c62b333f04f85dce74d17024ddf5fe8c3c2ccb6f4f0a86f4d44d29af84623b6a

Observation 1704f90c-2adc-4c8e-9df7-61bbdf05f0e7 · outbound

This paper cites Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization

Reference 13

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source=pdf_text observed=2026-08-07T13:51:35.615681Z digest=sha256:511b4c7d5bf82ab7de03d90d841fdb162dc559d599221e1a4ebd7881bd790ab4

Observation 08c56089-2de4-419e-a350-a52d56e437a0 · outbound

This paper cites An adaptive variational algorithm for exact molecular simulations on a quantum computer,.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation An adaptive variational algorithm for exact molecular simulations on a quantum computer,

Reference 14

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source=pdf_text observed=2026-08-07T13:51:35.777447Z digest=sha256:ea3f1a220a7965c5ae5d377193b173e07b397f2311766e6d26c671e8b975106e

Observation 1dbfd21e-4666-4138-b085-8b793e56f638 · outbound

This paper cites Adaptive quantum approximate op- timization algorithm for solving combinatorial problems on a quantum computer,.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Adaptive quantum approximate op- timization algorithm for solving combinatorial problems on a quantum computer,

Reference 15

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Observation 18f13a1a-efbd-4b15-b9f8-51f77da1c6e8 · outbound

This paper cites Machine learning method for state preparation and gate synthesis on photonic quantum computers,.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Machine learning method for state preparation and gate synthesis on photonic quantum computers,

Reference 16

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source=pdf_text observed=2026-08-07T13:51:36.110090Z digest=sha256:808775c5f9da2ee6813614e1c906b1c55f8b42787efb71c35a15a1ab866287f4

Observation 50d49ada-5f2e-40b6-8e25-16297754a59a · outbound

This paper cites Neural predictor based quantum architecture search,.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Neural predictor based quantum architecture search,

Reference 17

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T13:51:36.203348Z digest=sha256:2be9fe93ab1b36e9a077d4baeeca5d8ed31b9156136fd999d11527c52ce8e689

Observation 33bf9b75-e77c-422d-8791-4dcc13047045 · outbound

This paper cites Discovering Quantum Circuit Components with Program Synthesis.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Discovering Quantum Circuit Components with Program Synthesis

Reference 18

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source=pdf_text observed=2026-08-07T13:51:36.208389Z digest=sha256:b68880f55024543a562c800954853b7a05062fcb5ffc1a6cb7567832131b7066

Observation eb662906-3f98-46d8-800a-7028c45c9ebe · outbound

This paper cites Hybrid discrete-continuous compilation of trapped-ion quantum circuits with deep reinforcement learning,.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Hybrid discrete-continuous compilation of trapped-ion quantum circuits with deep reinforcement learning,

Reference 19

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doi, observed 2026-08-07T13:51:37.676890Z

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source=pdf_text observed=2026-08-07T13:51:36.228131Z digest=sha256:c7566b2be38834d6ac7f1f77cab9d7fbf83554e31581929d981b11df2f11b93f

Observation 496fe272-6409-4c7f-86f6-97219e772db9 · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics,.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Deep unsupervised learning using nonequilibrium thermodynamics,

Reference 20

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source=pdf_text observed=2026-08-07T13:51:36.258960Z digest=sha256:484fd1e0a7948cf4ec8b7fbcf64b577e71c684c5a0c48118a35430ad16130530

Observation e774c1f0-6eec-43e2-b5f9-0cb3488b5648 · outbound

This paper cites High- resolution image synthesis with latent diffusion models,.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation High- resolution image synthesis with latent diffusion models,

Reference 21

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source=pdf_text observed=2026-08-07T13:51:36.376405Z digest=sha256:8b01becf16bfdaf58541d27a3711ac2e569e1774dd0658261c5c6af51ddf14c7

Observation 47300e91-a773-437e-8004-77e683c21553 · outbound

This paper cites Video diffusion models,.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Video diffusion models,

Reference 22

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source=pdf_text observed=2026-08-07T13:51:36.458811Z digest=sha256:6f991039c11f7a8b56690b716b506ebbb075a0ec8adf1af0986d2c03f33fb88d

Observation dc7a02e7-59d4-4afa-b091-3fdfed8fa81e · outbound

This paper cites DiffWave: A Versatile Diffusion Model for Audio Synthesis.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation DiffWave: A Versatile Diffusion Model for Audio Synthesis

Reference 23

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source=pdf_text observed=2026-08-07T13:51:36.547628Z digest=sha256:fd383e4a97d16c2fe85ffb4e469b716b9b3b2a9f22f615062428e01a0e3bca1c

Observation cc87bebe-456e-4ada-a14a-e96a14a02d3d · outbound

This paper cites EigenFold: Generative Protein Structure Prediction with Diffusion Models.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation EigenFold: Generative Protein Structure Prediction with Diffusion Models

Reference 24

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source=pdf_text observed=2026-08-07T13:51:36.628780Z digest=sha256:d28a701f1349d220a160fbaa52ec501f2c108ff042f906598cf21b15ef5103ec

Observation a22384e1-c0aa-4271-87e2-f47b964c3a5b · outbound

This paper cites UDiTQC: U-Net-Style Diffusion Transformer for Quantum Circuit Synthesis.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation UDiTQC: U-Net-Style Diffusion Transformer for Quantum Circuit Synthesis

Reference 25

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source=pdf_text observed=2026-08-07T13:51:36.702072Z digest=sha256:0a2702e90941a23c2e44034decc7f42e1df26d53687b5fafdbf36b6dff257ff7

Observation 73c8abc0-d99c-4006-98e8-3da03e346c10 · outbound

This paper cites Prepare Ansatz for VQE with Diffusion Model.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Prepare Ansatz for VQE with Diffusion Model

Reference 26

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source=pdf_text observed=2026-08-07T13:51:36.805211Z digest=sha256:60f493f2370adfcf6debe712362e189567bcb2a790346eee9f59567b57c01df0

Observation 48e16d45-e35c-4d4f-bf57-fa005c74d5f0 · outbound

This paper cites Quantum circuit optimization with deep reinforcement learning.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Quantum circuit optimization with deep reinforcement learning

Reference 27

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source=pdf_text observed=2026-08-07T13:51:36.887098Z digest=sha256:d563ce9be7c0936a9fdd0b7bb58da317fa69d56dd072776e13c33545ae00a8c9

Observation 4a8026d9-9f6f-4749-b274-e33707c15d01 · outbound

This paper cites Quantum architecture search: a survey,.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Quantum architecture search: a survey,

Reference 28

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source=pdf_text observed=2026-08-07T13:51:36.984816Z digest=sha256:eeaf8e10db479bf89007d230c07daaa56cccafc4d54ecaf52358b1b709772505

Observation 33d987f8-ae74-42cd-be41-b9c6cd72b69b · outbound

This paper cites Challenges for reinforcement learning in quantum circuit design,.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Challenges for reinforcement learning in quantum circuit design,

Reference 29

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source=pdf_text observed=2026-08-07T13:51:37.056753Z digest=sha256:ee68b77d5e95ae7f4af0a3e54678b386a4dd527fec629aa2b1e15938feee2718

Observation ce8d4026-a515-4e87-a866-781fbc02dede · outbound

This paper cites The generative quantum eigensolver (gqe) and its application for ground state search,.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation The generative quantum eigensolver (gqe) and its application for ground state search,

Reference 30

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source=pdf_text observed=2026-08-07T13:51:37.129393Z digest=sha256:47e9a56583472dc85f6a8a64f49fd9d89485a17c4bc5530a93506cc160596966

Observation ad22dd51-59f2-4a00-99f1-e7d5faf3386b · outbound

This paper cites Denoising Diffusion Probabilistic Models.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Denoising Diffusion Probabilistic Models

Reference 31

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source=pdf_text observed=2026-08-07T13:51:37.226710Z digest=sha256:1f707909c3f9f5732a6bad56752bc32864b2d9af30fc6cfcd2b6769a7391c94b

Observation 0c2fae62-9572-44b4-9bae-1cdd4c55f13d · outbound

This paper cites Classifier-Free Diffusion Guidance.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Classifier-Free Diffusion Guidance

Reference 32

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source=pdf_text observed=2026-08-07T13:51:37.290729Z digest=sha256:9530cee34316464139ea91bc5bc0c7d923906d9ed340e311bb37a1fd3ed56d04

Observation 05bc4b42-59f3-47bb-a19c-79d8f01c8d0e · outbound

This paper cites Benchmarking quantum architecture search with surrogate assistance,.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Benchmarking quantum architecture search with surrogate assistance,

Reference 33

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T13:51:37.402624Z digest=sha256:3fbfe7a1923c7d3ec3c29734744fcfaa1afcc238f8909c0dfa1b395991d6d9f3

Observation 13e7b170-caae-4927-b26d-499e6afb9b13 · outbound

This paper cites Better than classical? The subtle art of benchmarking quantum machine learning models.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Better than classical? The subtle art of benchmarking quantum machine learning models

Reference 34

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source=pdf_text observed=2026-08-07T13:51:37.481164Z digest=sha256:9cd1b8faf77370127a4f10287f66c37e3ae2fcd4bcd94a70b1c459619098aaaf

Observation 7abe5509-1511-4710-a189-56f7229c980f · outbound

This paper cites Deep Unsupervised Learning using Nonequilibrium Thermodynamics.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Reference 2015

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source=pdf_text observed=2026-08-07T13:51:36.293477Z digest=sha256:f2b379dc80ce2573db4986700e667b7a8ac0e174ed8b7e6d2aee4207300cdcd9

Observation 8258f5a2-750b-4b5d-b540-9924c0417f10 · outbound

This paper cites Available: https://api.semanticscholar.org/CorpusID: 44098998.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Available: https://api.semanticscholar.org/CorpusID: 44098998

Reference 2018

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T13:51:33.973757Z digest=sha256:04e024cd647ad5fea769db49d8744307f6cc567a881abc98e7295e7db980d1ee

Observation f6f6223c-9b1e-47d5-9767-31cee888bc1c · outbound

This paper cites Available: https://api.semanticscholar.org/CorpusID: 265018897.

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation Available: https://api.semanticscholar.org/CorpusID: 265018897

Reference 2023

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:51:41.934245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T13:51:33.719726Z digest=sha256:93a9cbf6c1ac856608e0d3081b4c6b5a78a506a4ddd51c8903f94696eb4213af

Pith citing papers

Observation 3f7e2881-70e1-4690-b8f2-1ad7d1ff985d · inbound

From Characterization To Construction: Generative Quantum Circuit Synthesis from Gate Set Tomography Data cites this paper.

From Characterization To Construction: Generative Quantum Circuit Synthesis from Gate Set Tomography Data Leveraging Diffusion Models for Parameterized Quantum Circuit Generation

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-05-11T16:51:05.835357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-09T14:54:25.961118Z digest=sha256:aff1c2506db2514b38ee9a5d67ea0cb3532e50541cec1fd823281ee9ecf71c5c