Paper Citation Record · LEDGER

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs

As of 23 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2501.08678.

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

pith.paper-citation-record.v1
2501.08678 v3

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:23:54.898556Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

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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

28 of 28 outbound references displayed

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External citation measurements

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Outbound references

Observation fc13ee28-d2c9-4342-8ece-2adb4cf19c5b · outbound

This paper cites Generative adversarial nets,.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs Generative adversarial nets,

Reference 1

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Source-reported events for the cited work

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Observation ab5f81f4-3ee1-4041-b885-7b9b86f5dc3d · outbound

This paper cites Generative ad- versarial networks: introduction and outlook,.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs Generative ad- versarial networks: introduction and outlook,

Reference 2

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Observation ebbe7a7b-645f-4b46-b4d0-cfe7eba75a13 · outbound

This paper cites Generative adversarial networks: An overview,.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs Generative adversarial networks: An overview,

Reference 3

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This paper cites Generative adversarial networks,.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs Generative adversarial networks,

Reference 4

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This paper cites Gan-based synthetic brain pet image generation,.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs Gan-based synthetic brain pet image generation,

Reference 5

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Observation 4f684d9a-03f6-4a31-9ecc-34b4dd2e2bd4 · outbound

This paper cites Efficient and accurate mri super-resolution using a generative adversarial network and 3d multi- level densely connected network,.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs Efficient and accurate mri super-resolution using a generative adversarial network and 3d multi- level densely connected network,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:23:55.316825Z

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Observation c3a3d38e-b6ae-4384-ae17-ca11b7530bda · outbound

This paper cites Ge-gan: A novel deep learn- ing framework for road traffic state estimation,.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs Ge-gan: A novel deep learn- ing framework for road traffic state estimation,

Reference 7

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Observation c47586e8-6b17-48a8-a83c-cf51a3604a0d · outbound

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

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs MolGAN: An implicit generative model for small molecular graphs

Reference 8

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This paper cites Expressibility and entangling capa- bility of parameterized quantum circuits for hybrid quantum-classical algorithms,.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs Expressibility and entangling capa- bility of parameterized quantum circuits for hybrid quantum-classical algorithms,

Reference 9

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Source-reported events for the cited work

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Observation 1a400cbe-7961-469c-90a0-8dc9fb6d8f26 · outbound

This paper cites A survey of recent advances in quantum generative adversarial networks,.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs A survey of recent advances in quantum generative adversarial networks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:23:55.271180Z

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Observation 5a0f865d-996d-411c-9c42-36640abf7313 · outbound

This paper cites Quantum generative adversarial net- works,.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs Quantum generative adversarial net- works,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:23:55.252628Z

Source-reported events for the cited work

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Observation e167ec9e-bd90-4d07-81b4-cf311a057050 · outbound

This paper cites Quantum generative adversarial learning,.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs Quantum generative adversarial learning,

Reference 12

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Source-reported events for the cited work

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Observation c5d704c9-2f6c-46e0-b6f2-67eb12cb9676 · outbound

This paper cites To- wards quantum machine learning with tensor networks,.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs To- wards quantum machine learning with tensor networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:23:55.224508Z

Source-reported events for the cited work

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Observation b63be3a5-1190-487e-b9e9-2fc5e78f6cc9 · outbound

This paper cites Quantum wasserstein gen- erative adversarial networks,.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs Quantum wasserstein gen- erative adversarial networks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:23:55.208267Z

Source-reported events for the cited work

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Observation 8fbcab09-a64a-4aa8-88a9-e03218106d15 · outbound

This paper cites Quantum generative adversarial network for generating discrete distribution,.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs Quantum generative adversarial network for generating discrete distribution,

Reference 15

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Observation 5e364488-d1bd-483d-afb4-048089a58282 · outbound

This paper cites Quantum generative adversarial networks for learning and loading random distributions,.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs Quantum generative adversarial networks for learning and loading random distributions,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:23:55.174285Z

Source-reported events for the cited work

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Observation 80925132-d58a-4f7f-8ac3-107623337237 · outbound

This paper cites Qugan: A quantum state fidelity based generative adversarial network,.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs Qugan: A quantum state fidelity based generative adversarial network,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:23:55.157428Z

Source-reported events for the cited work

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Observation d4ca7f7a-25ab-4062-8c74-1c4af96b9b35 · outbound

This paper cites Exploring the advantages of quantum generative adversarial networks in generative chemistry,.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs Exploring the advantages of quantum generative adversarial networks in generative chemistry,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:23:55.141705Z

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Observation 43e0e10e-b451-4ab2-a63f-41675a8fb200 · outbound

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

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs Quantum generative models for small molecule drug discovery,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:23:55.125664Z

Source-reported events for the cited work

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Observation 26ff6f8c-2abe-478d-b2cb-5edce3cada77 · outbound

This paper cites Noise robustness and experimental demonstration of a quantum generative adversarial network for con- tinuous distributions,.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs Noise robustness and experimental demonstration of a quantum generative adversarial network for con- tinuous distributions,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:23:55.109252Z

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Observation 66544358-968d-4235-a9c2-e84f0babb2fe · outbound

This paper cites A survey on deep graph generation: Methods and applications,.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs A survey on deep graph generation: Methods and applications,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:23:55.090587Z

Source-reported events for the cited work

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Observation 06e7954f-a40f-4328-8b31-fd0254551cb1 · outbound

This paper cites The holy grail of quantum artificial intelligence: major challenges in accelerating the machine learning pipeline,.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs The holy grail of quantum artificial intelligence: major challenges in accelerating the machine learning pipeline,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:23:55.074302Z

Source-reported events for the cited work

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Observation de0e140e-fa13-45d6-bf13-b57ce703bb76 · outbound

This paper cites From Problem to Solution: A general Pipeline to Solve Optimisation Problems on Quantum Hardware.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs From Problem to Solution: A general Pipeline to Solve Optimisation Problems on Quantum Hardware

Reference 23

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Observation b9cf8650-ede5-4acd-b538-9a2e6a895237 · outbound

This paper cites searoute,.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs searoute,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:23:55.058587Z

Source-reported events for the cited work

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Observation 9d7ff8f9-4204-4fb9-89b3-b5ba7f11d1df · outbound

This paper cites Rectifier nonlinearities improve neural network acoustic models,.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs Rectifier nonlinearities improve neural network acoustic models,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:23:55.042990Z

Source-reported events for the cited work

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Observation 596e59e7-90ff-45bf-958b-28a643e5aea9 · outbound

This paper cites Introducing Reduced-Width QNNs, an AI-inspired Ansatz Design Pattern.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs Introducing Reduced-Width QNNs, an AI-inspired Ansatz Design Pattern

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-10T20:23:54.968308Z

Source-reported events for the cited work

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Observation 5cc7ea5f-2b53-4bcb-ab67-c40aa4c26f68 · outbound

This paper cites A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learning.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learning

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-10T20:23:54.943169Z

Source-reported events for the cited work

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Observation fb26c876-b606-4404-81ba-7b9b5eb3c8d4 · outbound

This paper cites The questionable influence of entanglement in quantum optimisation algorithms,.

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs The questionable influence of entanglement in quantum optimisation algorithms,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:23:55.026912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

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