Paper Citation Record · LEDGER

Quantum latent distributions in deep generative models

As of 20 August 2026, this Paper Citation Record lists 79 of 79 outbound references and 2 inbound Pith citation observations for arXiv:2508.19857.

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

pith.paper-citation-record.v1
2508.19857 v3

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Reference resolution

79 of 79 outbound references displayed

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External citation measurements

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Outbound references

Observation b5ae20ad-9e78-484b-87ab-d3fa873bc157 · outbound

This paper cites an unresolved cited work.

Quantum latent distributions in deep generative models Unresolved cited work

Reference 1

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Observation 89db5828-1184-40bf-a71d-9a0d6f258317 · outbound

This paper cites 1-1" configuration), and three delay lines in a.

Quantum latent distributions in deep generative models 1-1" configuration), and three delay lines in a

Reference 2

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This paper cites an unresolved cited work.

Quantum latent distributions in deep generative models Unresolved cited work

Reference 3

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Observation bf6a98e5-0ab7-49ff-8595-6dacfc5a857f · outbound

This paper cites Generative adversarial nets,.

Quantum latent distributions in deep generative models Generative adversarial nets,

Reference 4

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Observation 818ad641-2d1b-4d03-95cf-9cb8e1d2e133 · outbound

This paper cites A style-based generator architecture for generative adversarial networks,.

Quantum latent distributions in deep generative models A style-based generator architecture for generative adversarial networks,

Reference 5

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Observation edfc1727-1575-42c9-9af8-fa043a8166b3 · outbound

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

Quantum latent distributions in deep generative models High-resolution image synthesis with latent diffusion models,

Reference 6

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Observation cb12ace8-9bb9-4557-bbc4-07c7b050e4a0 · outbound

This paper cites Align your latents: High-resolution video synthesis with latent diffusion models,.

Quantum latent distributions in deep generative models Align your latents: High-resolution video synthesis with latent diffusion models,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:14.809994Z

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Observation 3becdcbf-9ccb-4d69-906e-dc6891be5676 · outbound

This paper cites Flow Matching for Generative Modeling.

Quantum latent distributions in deep generative models Flow Matching for Generative Modeling

Reference 8

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Observation fe53d484-7c3d-4018-bd89-49167d335134 · outbound

This paper cites Building Normalizing Flows with Stochastic Interpolants.

Quantum latent distributions in deep generative models Building Normalizing Flows with Stochastic Interpolants

Reference 9

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Observation f543840f-3454-4c74-b440-6b114bbc7a7e · outbound

This paper cites Auto-encoding variational Bayes,.

Quantum latent distributions in deep generative models Auto-encoding variational Bayes,

Reference 10

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Observation 8f606193-0af3-4739-9dcb-c9ecd172de8c · outbound

This paper cites Neural discrete representation learning,.

Quantum latent distributions in deep generative models Neural discrete representation learning,

Reference 11

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Observation cf74e76c-d42a-4a7c-a527-42bf745330a0 · outbound

This paper cites Associative adversarial networks,.

Quantum latent distributions in deep generative models Associative adversarial networks,

Reference 12

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Observation a5c21f8a-a221-4b03-91b0-81d7636569c5 · outbound

This paper cites Complexity matters: Rethinking the latent space for generative modeling,.

Quantum latent distributions in deep generative models Complexity matters: Rethinking the latent space for generative modeling,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:14.750544Z

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Observation f2aaaf06-effb-4af2-aacc-a5f4505475eb · outbound

This paper cites The computational complexity of linear optics,.

Quantum latent distributions in deep generative models The computational complexity of linear optics,

Reference 14

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Observation 64d191c1-3f53-4348-a9c1-d1b2f8a4b33b · outbound

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

Quantum latent distributions in deep generative models Generation of high-resolution handwritten digits with an ion-trap quantum computer,

Reference 15

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Observation cf6cd39b-0795-4931-b1c9-7782f5290ba8 · outbound

This paper cites Quantum supremacy using a programmable superconducting processor,.

Quantum latent distributions in deep generative models Quantum supremacy using a programmable superconducting processor,

Reference 16

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Observation 3cedfd78-5ce9-4535-92fe-767b324e4127 · outbound

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

Quantum latent distributions in deep generative models Quantum computational advantage with a programmable photonic processor,

Reference 17

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Observation 0a86adee-c659-4915-8517-41f83eea8a07 · outbound

This paper cites Computational advantage of quantum random sampling.

Quantum latent distributions in deep generative models Computational advantage of quantum random sampling

Reference 18

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Observation 8245ca0a-f98f-4dc5-a2b2-840084b63716 · outbound

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

Quantum latent distributions in deep generative models Quantum-assisted associative adversarial network: Applying quantum annealing in deep learning,

Reference 19

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Observation 827d2e92-1164-438c-a22a-a82efe46716d · outbound

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

Quantum latent distributions in deep generative models Quantum generative models for small molecule drug discovery,

Reference 20

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Observation c3dc14c9-3fee-471e-8731-f902bdb9814f · outbound

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

Quantum latent distributions in deep generative models Exploring the advantages of quantum generative adversarial networks in generative chemistry,

Reference 21

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Observation 5369165a-8511-4d72-9b99-86e6e0c3aa56 · outbound

This paper cites Quantum deep generative prior with programmable quantum circuits,.

Quantum latent distributions in deep generative models Quantum deep generative prior with programmable quantum circuits,

Reference 22

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Observation fc176dcc-bf8c-4d3f-ae4c-2be3e5cf3f2d · outbound

This paper cites Improving GANs by leveraging the quantum noise from real hardware.

Quantum latent distributions in deep generative models Improving GANs by leveraging the quantum noise from real hardware

Reference 23

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Observation 82b56f60-6009-4459-a722-fdfb816b23e0 · outbound

This paper cites Gaussian Mixture Generative Adversarial Networks for Diverse Datasets, and the Unsupervised Clustering of Images.

Quantum latent distributions in deep generative models Gaussian Mixture Generative Adversarial Networks for Diverse Datasets, and the Unsupervised Clustering of Images

Reference 24

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Observation 50d2688e-19d7-4ca2-955d-958ef592783c · outbound

This paper cites Clustergan: Latent space clustering in generative adversarial networks,.

Quantum latent distributions in deep generative models Clustergan: Latent space clustering in generative adversarial networks,

Reference 25

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Observation 11c2e0d9-c53b-4971-810b-3cc769ed253e · outbound

This paper cites Large scale GAN training for high fidelity natural image synthesis,.

Quantum latent distributions in deep generative models Large scale GAN training for high fidelity natural image synthesis,

Reference 26

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Observation a865ab3e-220d-472b-bf4a-3a13b4ac43ef · outbound

This paper cites BourGAN: Generative networks with metric embeddings,.

Quantum latent distributions in deep generative models BourGAN: Generative networks with metric embeddings,

Reference 27

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Observation 6531dd83-1e3d-4b54-9c0f-8b48f5d73917 · outbound

This paper cites Generalization Properties of Optimal Transport GANs with Latent Distribution Learning.

Quantum latent distributions in deep generative models Generalization Properties of Optimal Transport GANs with Latent Distribution Learning

Reference 28

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Observation b88d8544-3815-4c92-ad21-d11a82ddcb66 · outbound

This paper cites Adversarial Autoencoders.

Quantum latent distributions in deep generative models Adversarial Autoencoders

Reference 29

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Observation bddfd249-05f8-4c85-8b98-cb087330ef64 · outbound

This paper cites Improving and generalizing flow-based generative models with minibatch optimal transport.

Quantum latent distributions in deep generative models Improving and generalizing flow-based generative models with minibatch optimal transport

Reference 30

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This paper cites Quantum-computing-enhanced algorithm unveils potential KRAS inhibitors,.

Quantum latent distributions in deep generative models Quantum-computing-enhanced algorithm unveils potential KRAS inhibitors,

Reference 31

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Observation b0f41b78-78cd-4db3-b695-5664095685eb · outbound

This paper cites Quantum computational advantage using photons,.

Quantum latent distributions in deep generative models Quantum computational advantage using photons,

Reference 32

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Observation b9b9c7ec-5196-42a4-8e6a-2e7e4df42b45 · outbound

This paper cites The hardness of random quantum circuits,.

Quantum latent distributions in deep generative models The hardness of random quantum circuits,

Reference 33

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Observation 29302420-0bcd-43b1-ad55-1758da7dc6a6 · outbound

This paper cites BosonSampling Is Far From Uniform.

Quantum latent distributions in deep generative models BosonSampling Is Far From Uniform

Reference 34

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This paper cites Noise-induced barren plateaus in variational quantum algorithms,.

Quantum latent distributions in deep generative models Noise-induced barren plateaus in variational quantum algorithms,

Reference 35

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This paper cites Large-scale quantum reservoir computing using a Gaussian Boson Sampler.

Quantum latent distributions in deep generative models Large-scale quantum reservoir computing using a Gaussian Boson Sampler

Reference 36

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This paper cites Experimental quantum-enhanced kernels on a photonic processor.

Quantum latent distributions in deep generative models Experimental quantum-enhanced kernels on a photonic processor

Reference 37

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This paper cites Lipschitz regularity of deep neural networks: analysis and efficient estimation,.

Quantum latent distributions in deep generative models Lipschitz regularity of deep neural networks: analysis and efficient estimation,

Reference 38

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This paper cites The lipschitz constant of self-attention,.

Quantum latent distributions in deep generative models The lipschitz constant of self-attention,

Reference 39

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Observation 4ca83094-3b4a-4b05-9613-0a801a9bd580 · outbound

This paper cites Spectrally-normalized margin bounds for neural networks,.

Quantum latent distributions in deep generative models Spectrally-normalized margin bounds for neural networks,

Reference 40

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Observation 06be543e-d8ff-4559-94dd-9014456c287e · outbound

This paper cites Relational inductive biases, deep learning, and graph networks.

Quantum latent distributions in deep generative models Relational inductive biases, deep learning, and graph networks

Reference 41

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Observation 0f8f124a-5918-4830-9877-7803adb872ce · outbound

This paper cites Challenging common assumptions in the unsupervised learning of disentangled representations,.

Quantum latent distributions in deep generative models Challenging common assumptions in the unsupervised learning of disentangled representations,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:14.430770Z

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Observation 936f893d-87e3-4830-bb3c-60d9c5de1cf1 · outbound

This paper cites Lost in latent space: Examining failures of disentangled models at combinatorial generalisation,.

Quantum latent distributions in deep generative models Lost in latent space: Examining failures of disentangled models at combinatorial generalisation,

Reference 43

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Observation c433470e-9e96-494f-9e1b-eaa315bcd4e3 · outbound

This paper cites Learning factorized multimodal representations,.

Quantum latent distributions in deep generative models Learning factorized multimodal representations,

Reference 44

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Observation ac9e8456-bb58-4283-ac92-31a85900c9b6 · outbound

This paper cites InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets,.

Quantum latent distributions in deep generative models InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:14.377149Z

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Observation 13a12221-9ad1-4ba5-b072-778f400dbc27 · outbound

This paper cites Exact gradients for linear optics with single photons.

Quantum latent distributions in deep generative models Exact gradients for linear optics with single photons

Reference 46

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Observation e6bd0707-6e88-4ff7-953e-d6f2c25c6c33 · outbound

This paper cites Barren plateaus in quantum neural network training landscapes,.

Quantum latent distributions in deep generative models Barren plateaus in quantum neural network training landscapes,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:14.360529Z

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Observation c727d843-947a-4406-b80c-5f1ec2cd5cd2 · outbound

This paper cites The classical complexity of boson sampling,.

Quantum latent distributions in deep generative models The classical complexity of boson sampling,

Reference 48

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Observation dbc10323-5e6a-4a53-945a-1104a6aab579 · outbound

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

Quantum latent distributions in deep generative models Quantum chemistry structures and properties of 134 kilo molecules,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:14.322190Z

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Observation 1e0e4826-68a6-483b-b152-05041373dda6 · outbound

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

Quantum latent distributions in deep generative models MolGAN: An implicit generative model for small molecular graphs

Reference 50

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Observation 47fdcaeb-9722-4469-9d5f-94088322dbb3 · outbound

This paper cites Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models.

Quantum latent distributions in deep generative models Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models

Reference 51

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Observation e14dde31-931c-416c-b690-34e87467e279 · outbound

This paper cites TenGAN: Pure transformer encoders make an efficient discrete gan for de novo molecular generation,.

Quantum latent distributions in deep generative models TenGAN: Pure transformer encoders make an efficient discrete gan for de novo molecular generation,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:14.292829Z

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Observation 50e2b280-47b7-43ed-90b0-c18aba1fef7d · outbound

This paper cites Fréchet ChemNet distance: a metric for generative models for molecules in drug discovery,.

Quantum latent distributions in deep generative models Fréchet ChemNet distance: a metric for generative models for molecules in drug discovery,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:14.274525Z

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Observation 4d8cddee-4782-4aec-a66f-44e096071e37 · outbound

This paper cites Quantum computational advantage via high-dimensional Gaussian boson sampling,.

Quantum latent distributions in deep generative models Quantum computational advantage via high-dimensional Gaussian boson sampling,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:14.254404Z

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Observation 27ab02ef-1ffd-4d4f-b58c-8bfc05a552c1 · outbound

This paper cites Boundaries for quantum advantage with single photons and loop-based time-bin interferometers,.

Quantum latent distributions in deep generative models Boundaries for quantum advantage with single photons and loop-based time-bin interferometers,

Reference 55

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Observation 8575ded1-9cba-4b3b-b1e1-25e120db24c8 · outbound

This paper cites Diffusion models beat GANs on image synthesis,.

Quantum latent distributions in deep generative models Diffusion models beat GANs on image synthesis,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:14.235401Z

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Observation 7ad448f6-4b11-4779-ba22-acb7a0560a19 · outbound

This paper cites Accurate prediction of protein structures and interactions using a three-track neural network,.

Quantum latent distributions in deep generative models Accurate prediction of protein structures and interactions using a three-track neural network,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:14.217497Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7bdbf90e-032d-431f-9e62-92b35b2e4f32 · outbound

This paper cites Tackling the Generative Learning Trilemma with Denoising Diffusion GANs.

Quantum latent distributions in deep generative models Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-05T15:33:13.293815Z

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Observation 64f94ffb-2cf4-4506-95cf-3fce29594f9a · outbound

This paper cites Learning multiple layers of features from tiny images,.

Quantum latent distributions in deep generative models Learning multiple layers of features from tiny images,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-05T15:33:13.298732Z

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Observation be8b0131-79b6-4b80-9195-ffead0a75c97 · outbound

This paper cites Hybrid classical-quantum supercomputing: A demonstration of a multi-user, multi-qpu and multi-gpu environment,.

Quantum latent distributions in deep generative models Hybrid classical-quantum supercomputing: A demonstration of a multi-user, multi-qpu and multi-gpu environment,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:14.183953Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cea4502c-845d-4465-a8c1-80a1d40f02bf · outbound

This paper cites Efficient approximation of experimental Gaussian boson sampling.

Quantum latent distributions in deep generative models Efficient approximation of experimental Gaussian boson sampling

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-05T15:33:13.309402Z

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Observation add75993-99ee-4100-88d6-05b6e3a017d0 · outbound

This paper cites Classical algorithm for simulating experimental gaussian boson sampling,.

Quantum latent distributions in deep generative models Classical algorithm for simulating experimental gaussian boson sampling,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:14.163156Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1d132e4d-bb0c-426a-a48e-524ac33328f2 · outbound

This paper cites an unresolved cited work.

Quantum latent distributions in deep generative models Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-05T15:33:14.144145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ed5bf53a-ba63-46da-b694-e0179c00d0a1 · outbound

This paper cites As such we reach the same conclusions on complexity since Poly(n, 1/cϵ) ≡ Poly(n, 1/ϵ) for some constantc.

Quantum latent distributions in deep generative models As such we reach the same conclusions on complexity since Poly(n, 1/cϵ) ≡ Poly(n, 1/ϵ) for some constantc

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:14.128796Z

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Observation 30e6ebf5-0bf9-4ce1-ba8a-043d4f7a2b6e · outbound

This paper cites Boson sampling on a photonic chip,.

Quantum latent distributions in deep generative models Boson sampling on a photonic chip,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:14.111372Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 40d8ac9f-aa59-4a46-9f17-9c4e8a043a3f · outbound

This paper cites Robust quantum computational advantage with programmable 3050-photon Gaussian boson sampling.

Quantum latent distributions in deep generative models Robust quantum computational advantage with programmable 3050-photon Gaussian boson sampling

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-05T15:33:13.333654Z

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Observation 3950ae4e-776a-4fc3-9aa7-eb75d6a765af · outbound

This paper cites Improved training of Wasserstein GANs,.

Quantum latent distributions in deep generative models Improved training of Wasserstein GANs,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:14.092287Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4290e545-5a94-48b5-b70b-8dbd6e519b00 · outbound

This paper cites Analyzing and improving the image quality of stylegan,.

Quantum latent distributions in deep generative models Analyzing and improving the image quality of stylegan,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:14.074570Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 71553b2c-e81f-4b4b-89f9-6c4f51862b00 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Quantum latent distributions in deep generative models Adam: A Method for Stochastic Optimization

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-05T15:33:13.347515Z

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Observation f29665d3-a444-4760-bd8f-ee04f2afb76b · outbound

This paper cites Improved techniques for training GANs,.

Quantum latent distributions in deep generative models Improved techniques for training GANs,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:14.057765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4d92a4d4-dc84-4791-afcb-972a3af4866f · outbound

This paper cites an unresolved cited work.

Quantum latent distributions in deep generative models Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-08-05T15:33:14.041221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d4987a07-7fe8-4886-8397-d0d868ae3530 · outbound

This paper cites For instance, [29] performed a boson sampling experiment in which up to 76 photons were measured in 100 channels using a fixed interference circuit.

Quantum latent distributions in deep generative models For instance, [29] performed a boson sampling experiment in which up to 76 photons were measured in 100 channels using a fixed interference circuit

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:14.026834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5ae533ac-899a-4e8c-a236-87e69a77dfc2 · outbound

This paper cites an unresolved cited work.

Quantum latent distributions in deep generative models Unresolved cited work

Reference 73

Resolution: unresolved; raw_fallback, observed 2026-08-05T15:33:14.010987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation eeb47eae-050b-4ffc-bfdf-521bb11bf92b · outbound

This paper cites permutation probability.

Quantum latent distributions in deep generative models permutation probability

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:13.993754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2ab1bc99-b792-4025-8457-1eaca3b8cbda · outbound

This paper cites This is the case for all experiments with a toy dataset, and also for all size-16 and most size-32 latents on the QM9 dataset.

Quantum latent distributions in deep generative models This is the case for all experiments with a toy dataset, and also for all size-16 and most size-32 latents on the QM9 dataset

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:13.975037Z

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Observation f92c4380-06a0-444e-93fc-63f328ac9d6d · outbound

This paper cites It took 40 minutes to collect 500k samples with an ORCA PT-2.

Quantum latent distributions in deep generative models It took 40 minutes to collect 500k samples with an ORCA PT-2

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:13.956210Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4fe42135-9f73-46e4-bafa-9a76c54e9edf · outbound

This paper cites Appendix G: Flow matching We build on the Optimal-Transport variant of Conditional Flow Matching from [27], as implemented inhttps:// github.com/atong01/conditional-flow-matching.

Quantum latent distributions in deep generative models Appendix G: Flow matching We build on the Optimal-Transport variant of Conditional Flow Matching from [27], as implemented inhttps:// github.com/atong01/conditional-flow-matching

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:13.939889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d1438a1c-e896-4f0f-a0bc-6ce42c17b6c0 · outbound

This paper cites We used the inception score [67] as the metric for model performance.

Quantum latent distributions in deep generative models We used the inception score [67] as the metric for model performance

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:13.923166Z

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Observation 61b40bc9-f019-47ac-8182-9a9fcb9a3832 · outbound

This paper cites an unresolved cited work.

Quantum latent distributions in deep generative models Unresolved cited work

Reference 79

Resolution: unresolved; raw_fallback, observed 2026-08-05T15:33:13.903939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation f26a8262-1f3b-4454-9592-c4ba4ed235dd · inbound

Quantum Fourier Generative Models Trainable at Large Scale cites this paper.

Quantum Fourier Generative Models Trainable at Large Scale Quantum latent distributions in deep generative models

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-07-01T15:25:48.658105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b9bfc11d-2bd1-4b4e-a30c-eda757009b1e · inbound

The trainability of photonic quantum circuits cites this paper.

The trainability of photonic quantum circuits Quantum latent distributions in deep generative models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-01T07:13:14.323657Z

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