Paper Citation Record · LEDGER

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning

As of 7 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 2 inbound Pith citation observations for arXiv:2509.05051.

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

pith.paper-citation-record.v1
2509.05051 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T05:42:33.224597Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T03:54:30.070539Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

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verified fuzzy31
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f779a847-b8c4-4457-9235-443fce089ba3 · outbound

This paper cites Improving drug candidates by design: a focus on physicochemical properties as a means of improving compound dis- position and safety,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Improving drug candidates by design: a focus on physicochemical properties as a means of improving compound dis- position and safety,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:34.552319Z

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Observation 4eba4142-37ff-4468-9413-2b19d28ff2db · outbound

This paper cites Deep reinforcement learning for de novo drug design,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Deep reinforcement learning for de novo drug design,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:34.526085Z

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Observation cba15930-9d85-4395-af6c-fe8047f29031 · outbound

This paper cites Inverse design in search of materials with target function- alities,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Inverse design in search of materials with target function- alities,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:34.499141Z

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Observation ecbb6298-2dd3-49bf-bed0-124607db9a30 · outbound

This paper cites Virtual compound libraries in computer-assisted drug discovery,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Virtual compound libraries in computer-assisted drug discovery,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:34.473968Z

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Observation 1aeba0c5-f355-4a5c-8004-2225caba1c3e · outbound

This paper cites Principles of early drug discovery,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Principles of early drug discovery,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:34.443991Z

Source-reported events for the cited work

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Observation 1f9d7730-3820-44c3-80aa-3ebcfdf4c7e0 · outbound

This paper cites Applications of machine learning in drug discovery and development,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Applications of machine learning in drug discovery and development,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:34.419679Z

Source-reported events for the cited work

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Observation 18d997ab-210a-466f-9972-6ddabc1129c6 · outbound

This paper cites Machine learning in drug discovery: a review,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Machine learning in drug discovery: a review,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:34.395764Z

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Observation 3f620db6-e1c1-45bd-a022-ada02e964206 · outbound

This paper cites Generative adversarial nets,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Generative adversarial nets,

Reference 8

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Observation abffe472-5c60-42c8-9d3a-e87975c7c4d0 · outbound

This paper cites Auto-Encoding Variational Bayes.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Auto-Encoding Variational Bayes

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T05:42:32.955573Z

Source-reported events for the cited work

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Observation 3c22411e-be75-4b8c-9851-c62e0c7c3817 · outbound

This paper cites Recurrent neural networks,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Recurrent neural networks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:34.351130Z

Source-reported events for the cited work

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Observation 8d2c7a3f-10c2-4921-be06-acb2dc5d02a6 · outbound

This paper cites Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T05:42:32.968786Z

Source-reported events for the cited work

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Observation 7440cb77-3736-4012-bbdc-c73e24e01da5 · outbound

This paper cites Quantitative structure- activity relationship methods: Perspectives on drug discovery and tox- icology,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Quantitative structure- activity relationship methods: Perspectives on drug discovery and tox- icology,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:34.281797Z

Source-reported events for the cited work

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Observation 1f588d98-230b-41f6-9b1d-ddff6fed42e1 · outbound

This paper cites MolGAN: An implicit generative model for small molecular graphs.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning MolGAN: An implicit generative model for small molecular graphs

Reference 13

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Observation 1da13f00-a0af-4e91-8112-4553dbe4df3f · outbound

This paper cites Temporally unstructured quantum computation,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Temporally unstructured quantum computation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:34.244278Z

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Observation b12e5195-447f-4837-9b3f-2f0a4b73489e · outbound

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

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Quantum computing in the nisq era and beyond,

Reference 15

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Source-reported events for the cited work

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Observation 6b42b190-519f-4211-97e6-3cd69e350199 · outbound

This paper cites Quantum machine learning,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Quantum machine learning,

Reference 16

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Source-reported events for the cited work

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Observation 3def14fb-6372-4800-91a9-9976f1f57c0f · outbound

This paper cites Quantum computational advantage with a programmable photonic processor,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Quantum computational advantage with a programmable photonic processor,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:34.176714Z

Source-reported events for the cited work

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Observation d79fbf1d-ff85-4043-85ea-8738b7491ca8 · outbound

This paper cites Quantum generative models for small molecule drug discovery,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Quantum generative models for small molecule drug discovery,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:34.149972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 130b5147-3310-44bf-a783-dfbb3ea71145 · outbound

This paper cites Hybrid quantum-classical machine learning for generative chemistry and drug design,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Hybrid quantum-classical machine learning for generative chemistry and drug design,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:34.124265Z

Source-reported events for the cited work

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Observation efb628e5-8eca-47fe-b3cd-53caa5570a4a · outbound

This paper cites Ex- ploring the advantages of quantum generative adversarial networks in generative chemistry,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Ex- ploring the advantages of quantum generative adversarial networks in generative chemistry,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:34.093794Z

Source-reported events for the cited work

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Observation ec140c11-6e85-4527-a417-f73a6d5fbfa8 · outbound

This paper cites Hybrid quantum cycle generative ad- versarial network for small molecule generation,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Hybrid quantum cycle generative ad- versarial network for small molecule generation,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:34.051154Z

Source-reported events for the cited work

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Observation f36d150f-a559-4700-9f42-454fb1ebff45 · outbound

This paper cites Quantum Machine Learning For Classical Data.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Quantum Machine Learning For Classical Data

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-05T05:42:33.439027Z

Source-reported events for the cited work

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Observation eb348898-6aed-43b1-8654-00895b70c8c6 · outbound

This paper cites A review on mode collapse reducing gans with gan’s algorithm and theory,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning A review on mode collapse reducing gans with gan’s algorithm and theory,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:34.025376Z

Source-reported events for the cited work

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Observation 72157ca7-064f-4293-92dc-b510a809230f · outbound

This paper cites L- molgan: An improved implicit generative model for large molecular graphs,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning L- molgan: An improved implicit generative model for large molecular graphs,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:33.994052Z

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Observation 8df6e664-2ef4-4388-9037-db57703e6998 · outbound

This paper cites A comprehensive survey on graph neural networks,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning A comprehensive survey on graph neural networks,

Reference 25

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Observation cae9eb50-4728-4cbf-bdae-76210ebe4ce1 · outbound

This paper cites Graph neural networks: A review of methods and applications,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Graph neural networks: A review of methods and applications,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:33.956385Z

Source-reported events for the cited work

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Observation 9ecf480c-3eff-4182-aebc-06fd7dd3816b · outbound

This paper cites Modeling relational data with graph convolutional networks,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Modeling relational data with graph convolutional networks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:33.920048Z

Source-reported events for the cited work

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Observation e34360ec-48c4-41e7-8688-b9816466e85d · outbound

This paper cites Games of gans: Game-theoretical models for generative adversarial networks,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Games of gans: Game-theoretical models for generative adversarial networks,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:33.892996Z

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Observation 331bb897-8e1b-4d79-a9cb-c91808eb583c · outbound

This paper cites Exploration in deep rein- forcement learning: A survey,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Exploration in deep rein- forcement learning: A survey,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:33.868144Z

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Observation 47b20c4c-8d45-48f9-8003-11309d615db5 · outbound

This paper cites Deterministic policy gradient algorithms,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Deterministic policy gradient algorithms,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:33.843982Z

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Observation 7ba3a75a-4dc8-45d5-b7ee-8af458c0d728 · outbound

This paper cites Continuous control with deep reinforcement learning.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Continuous control with deep reinforcement learning

Reference 31

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Observation 120cd722-7f70-43f5-b040-46fd8e131036 · outbound

This paper cites Quantifying the chemical beauty of drugs,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Quantifying the chemical beauty of drugs,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:33.815900Z

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Observation f3d40711-3b9c-43ff-a69a-15cb7644680b · outbound

This paper cites Lipophilicity profiles: theory and measurement,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Lipophilicity profiles: theory and measurement,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:33.789587Z

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Observation 7eb6adb1-4be9-4b95-999c-2ca4af9daeb3 · outbound

This paper cites Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:33.760236Z

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Observation 428cc005-ed7e-412e-9df2-631814af10f4 · outbound

This paper cites Associative Adversarial Networks.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Associative Adversarial Networks

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-05T05:42:33.350361Z

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Observation 0903eef0-7781-49d4-a0a7-5c371a5a2f9e · outbound

This paper cites A learning algorithm for boltzmann machines,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning A learning algorithm for boltzmann machines,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:33.731396Z

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Observation 448e8e13-c000-4310-a066-0bb085959ddf · outbound

This paper cites Quantum-assisted associative adversarial network: Applying quantum annealing in deep learning,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Quantum-assisted associative adversarial network: Applying quantum annealing in deep learning,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:33.705676Z

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Observation d7f7d1af-9c56-409c-b48c-814f4edd80b0 · outbound

This paper cites Generation of high-resolution handwritten digits with an ion-trap quantum computer,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Generation of high-resolution handwritten digits with an ion-trap quantum computer,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:33.679186Z

Source-reported events for the cited work

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Observation 877c6457-f9b0-43f2-ae34-8fee237c81af · outbound

This paper cites Comparing the effects of boltzmann machines as associative memory in generative adversarial networks between classical and quantum samplings,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Comparing the effects of boltzmann machines as associative memory in generative adversarial networks between classical and quantum samplings,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:33.653479Z

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Observation 8fa0173d-e169-4061-be49-428dfb1e355d · outbound

This paper cites Improved training of wasserstein gans,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Improved training of wasserstein gans,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-05T05:42:33.195019Z

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Observation e323f19f-a80c-4a51-8891-d12e6744a373 · outbound

This paper cites Molecular Generative Adversarial Network with Multi-Property Optimization.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Molecular Generative Adversarial Network with Multi-Property Optimization

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-08-05T05:42:33.300855Z

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Observation a94c309d-a06d-4ae2-a6a1-586d4bdd51cb · outbound

This paper cites Quantum chemistry structures and properties of 134 kilo molecules,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Quantum chemistry structures and properties of 134 kilo molecules,

Reference 42

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Source-reported events for the cited work

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Observation 4028195d-9748-48af-ad59-f1f7e457b743 · outbound

This paper cites Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:33.571409Z

Source-reported events for the cited work

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Observation 8a2e1cb0-ac32-4291-9d7d-9dd8c3624474 · outbound

This paper cites Efficient global optimization using spsa,.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning Efficient global optimization using spsa,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:42:33.540636Z

Source-reported events for the cited work

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Pith citing papers

Observation 0ad6f8f1-0981-43b6-a29a-7aa4a19035cc · inbound

Rank-Refined Quantum-Behaved Particle Swarm Optimization for Quantum Molecular Generation cites this paper.

Rank-Refined Quantum-Behaved Particle Swarm Optimization for Quantum Molecular Generation QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning

Reference 21

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Observation f82fad6f-9395-4ba4-869c-a71bcb2600b3 · inbound

Implementations of Quantum and Classical Topology-Aligned Architectures for Molecular Property Prediction cites this paper.

Implementations of Quantum and Classical Topology-Aligned Architectures for Molecular Property Prediction QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T03:54:30.070539Z

Source-reported events for the cited work

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