Pith. sign in

REVIEW 5 major objections 6 minor 1 cited by

A Survey of Quantum Generative Adversarial Networks: Architectures, Use Cases, and Real-World Implementations

T0 review · 5 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This survey organizes quantum generative adversarial networks by architecture, application, and hardware, and it argues that hybrid quantum-classical designs are the practical route forward.

desk verdict A useful recent-works survey of QGANs, but the citation-to-claim mapping has several load-bearing errors and the scope is narrower than the abstract claims. read the letter →

arxiv 2506.18002 v1 pith:KRU2P33C submitted 2025-06-22 quant-ph

Mujahidul Islam , Serkan Turkeli , Fatih Ozaydin This is my paper
classification quant-ph
keywords quantumgenerativeadversarialnetworksQGANmachinelearningvariationalcircuitshybridquantum-classicalalgorithmsNISQhardwaremodelingerrormitigation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Quantum generative adversarial networks (QGANs) put the adversarial generator-discriminator game of classical GANs on quantum hardware. This survey's central claim is that the resulting literature has a clear structure: architectures divide by where the quantum circuits sit, with fully quantum, hybrid quantum-classical, and quantum-assisted designs, and the hybrid form is the practical mainstream. It argues that the field has moved from theoretical proposals around 2018 to working demonstrations on superconducting, photonic, trapped-ion, and Rydberg processors, applied to image synthesis, medical signals, molecular design, finance, and communications. The survey also identifies the obstacles that decide near-term feasibility: quantum noise, barren plateaus, and limited qubit counts, together with mitigation strategies such as zero-noise extrapolation and noise-aware training. If its map is accurate, it gives researchers a reliable orientation for where QGANs stand and what would have to improve for practical advantage.

What carries the argument

The load-bearing object is the adversarial minimax objective extended to quantum circuits: $V(D,G)=\mathbb{E}_{x\sim p_{\mathrm{data}}(x)}[\log D(x)]+\mathbb{E}_{z\sim p_z(z)}[\log(1-D(G(z)))]$, where the generator $G(\theta)$ is a parameterized quantum circuit preparing a state and the discriminator $D(\phi)$ is a quantum circuit or classical network that measures how well generated samples match real data. Training relies on variational optimization of $\theta$ and $\phi$, typically through the parameter-shift rule for exact gradients, alongside gradient-free and hybrid classical optimizers. The taxonomy built around this mechanism, fully quantum, hybrid, and quantum-assisted, is what carries the argument that the field has a coherent structure and a known set of bottlenecks, including barren-plateau gradient decay, readout and gate-error noise thresholds, and hardware connectivity limits.

What would settle it

Check the primary sources behind the survey's key claims: verify whether reference [12] concerns quantum Boltzmann machines and reference [33] a trapped-ion implementation, then examine the hardware results it highlights, such as the claimed 33% Frechet Inception Distance improvement of the Rydberg demonstration over the superconducting baseline. If a substantial share of citation-to-claim links fail, the survey's map is unreliable; if the reported hardware metrics do not match the originals, its feasibility assessment is overstated.

Watch

Extended reading notes

Core claim

On its own terms, this is a taxonomy and a status report. The paper claims that QGANs extend the classical minimax game, in which a generator creates synthetic samples and a discriminator judges them, to settings where at least one player is a parameterized quantum circuit; fully quantum versions use quantum data and measurement, while hybrid versions pair a quantum generator with a classical discriminator or the reverse. The survey attaches to each architectural family its main application areas and hardware demonstrations, and it asserts that the experimental record, though small-scale, shows QGANs can be trained on real noisy devices when error mitigation is applied. Its synthesis is that QGANs are a flexible and viable framework for generative modeling on near-term hardware, with practical quantum advantage still unproven but actively pursued.

Load-bearing premise

The survey's usefulness depends on every cited reference actually supporting the specific claim it is attached to; references [12] and [33] already fail that test, so the mapping is not uniformly reliable.

Editorial extensions

If this is right

  • If the survey's picture is right, near-term QGAN work will continue to favor hybrid quantum-classical architectures, because fully quantum implementations need more qubits and lower noise than current devices offer.
  • Image generation will likely remain the benchmark task, so progress can be tracked by standard metrics such as Frechet Inception Distance across superconducting, photonic, and Rydberg demonstrations.
  • Error mitigation is a necessary ingredient: zero-noise extrapolation, noise-aware training, and readout bit-flip averaging are among the techniques the field will need to make trained QGANs usable on noisy hardware.
  • The trend toward conditional QGANs and domain-specific integration, with Wasserstein losses, optimization heuristics, and large-language-model ansatz design, should accelerate because those extensions address stability and scalability directly.
  • Standardized cross-platform benchmarks are a necessary next step before claims of practical quantum advantage can be evaluated.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A useful extension beyond the paper would be to test its taxonomy against future literature: if fully quantum designs or non-image applications overtake hybrid image-generation work, the claimed trajectory would need revising.
  • The survey's application breadth suggests QGANs are being adopted mainly as data-augmentation tools; whether that role yields genuine quantum advantage is a question the survey does not settle.
  • As photonic and Rydberg platforms mature, comparisons between QGAN implementations will likely shift from qubit counts to connectivity, gate fidelity, and coherence, the dimensions on which those platforms differ most.
  • An audit of the survey's citation-to-claim mapping would be a small, concrete project that would tell a reader which parts of the map to trust without re-reading all primary sources.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. This manuscript is a survey of quantum generative adversarial networks (QGANs). It reviews the classical GAN formalism and its quantum generalization, describes common quantum circuit architectures (variational circuits, Born machines, Boltzmann machines, convolutional networks), discusses training methods and optimization challenges such as barren plateaus and quantum natural gradient, categorizes implementations as fully quantum, hybrid quantum-classical, or quantum-enhanced, and surveys hardware platforms and application domains including image generation, drug discovery, finance, and scientific simulation. The final section gives a chronological account of works from 2023 to 2025, and the conclusion lists open challenges and future directions.

Significance. The survey fills a useful niche by cataloging very recent QGAN work, including hardware demonstrations on photonic chips, Rydberg atom processors, and trapped-ion systems, and by connecting QGANs to application-specific techniques such as Wasserstein losses, LLM-based ansatz design, and error mitigation. Its organizational scheme (fully quantum vs hybrid vs quantum-assisted) is sensible and would be a helpful entry point for newcomers. The value of the survey, however, depends on the accuracy of its reference-to-claim mapping, and that mapping currently has several load-bearing failures. The survey is therefore useful in conception but cannot be recommended in its present form.

major comments (5)
  1. [§2.2, Eq. (6)] The Quantum Boltzmann Machine architecture is attributed to reference [12], Khoshaman et al., 'Quantum variational autoencoder.' That paper does not propose or review QBMs; it is a variational autoencoder. The QBM definition should cite an appropriate QBM reference or be removed.
  2. [§3.3, Trapped Ion Systems] The text states that a few papers explore trapped-ion implementations and cites [33], Arute et al., 'Quantum supremacy using a programmable superconducting processor.' This is a superconducting processor paper and does not support the trapped-ion claim. Notably, the survey later discusses a trapped-ion QGAN in §6 via [85] (Sekwao et al.), which would be a correct citation here.
  3. [Abstract and §6] The abstract promises a 'comprehensive overview ... as of 2025,' but §6 explicitly limits coverage to works since 2023, deferring earlier work to reference [59]. This scope restriction is reasonable, but the abstract and introduction should state it; as written, the title and abstract overclaim completeness. Please revise the scope statement to match the actual coverage.
  4. [§2.3, Eq. (11)] The quantum Fisher information matrix is attributed to references [22,23], which are two-qubit metrology papers by the authors. Those papers may be relevant to QFI estimation, but they are not the standard references for the quantum Fisher information matrix in the natural-gradient context; a general reference suitable for the optimization setting would be more appropriate. Please also consider whether self-citations in background sections are necessary.
  5. [§5.3, last paragraph] The discussion of quantum Zeno dynamics cites references [55-58], none of which are QGAN papers. The suggestion that Zeno dynamics 'may offer a promising approach' for QGAN circuit complexity is speculative and is not supported by the cited works. This passage should be either removed or clearly labeled as an open suggestion with a concrete mechanism connecting Zeno dynamics to QGAN training.
minor comments (6)
  1. [§5.1, Eq. (19)] The word 'qhere' should be 'where'.
  2. [References, [3]] The conference title contains a typo: 'Beural Networks' should be 'Neural Networks.'
  3. [§4.1, text around Eq. (16)] Equation (16) duplicates Eq. (5); the later appearance could simply refer back to Eq. (5) rather than restating the same expression.
  4. [§4.1, hybrid approaches] Reference [27] is cited both for entanglement entropy and for hybrid high-resolution image generation; [27] (Niu et al.) is appropriate for entangling QGANs, but the sentence about upscaling by classical neural networks should be checked against the cited paper.
  5. [References, [79]] Reference [79] is cited in support of the pix2pix architecture, but [79] is a CycleGAN-based face image translation paper; please correct the citation or rephrase the sentence.
  6. [Author affiliations] The affiliation block contains inconsistent and incomplete address fields (e.g., 'State, Japan', 'Country', and an apparent mismatch in the corresponding author email); these should be cleaned before publication.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: this survey makes no derived predictions or fitted claims, and the cited self-works are non-load-bearing attributions.

full rationale

This manuscript is a survey, so it contains no derivation chain whose output could coincide with its inputs. It fits no parameters, computes no novel predictions, and proves no theorems; its central claim is taxonomic and descriptive, namely categorizing QGAN architectures and summarizing applications and hardware implementations. The authors' own prior work appears in two background contexts. In Section 2.3, references [22,23] are cited for the standard quantum Fisher information matrix appearing in the quantum natural gradient update, Eqs. (10)-(11). That formula is a well-known externally verifiable result, and the citation does not define the survey's target claim. In Section 5.3, references [56-58] are cited as examples where quantum Zeno dynamics reduced circuit complexity or enabled entanglement advantages, followed by the explicitly hedged suggestion that such dynamics 'may offer a promising approach for mitigating circuit complexity limitations in QGAN implementations.' This is a speculative future-direction remark, not a load-bearing derivation, and it does not import the survey's conclusion from the authors' own prior work. The scope narrowing in Section 6, where the authors say they 'focus on works since then' after mentioning earlier reviews, is transparent and is a coverage decision rather than a circularity. The identifiable weaknesses are citation-to-claim accuracy errors: reference [12] is a quantum variational autoencoder paper cited for Quantum Boltzmann Machines, and reference [33] is a superconducting-processor paper cited for trapped-ion systems. These are correctness and reliability concerns for a survey, not circular reductions, because they do not make any derived result equivalent to its own input. Accordingly, the circularity score is 0.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The survey's claims rest on a small set of assumptions: faithful representation of cited works and a sound taxonomy. The mis-citations show the first is partially violated, and the Zeno aside shows a self-referential assumption. No free parameters or invented entities are present.

assumptions (2)
  • domain assumption The surveyed papers' reported results and performance numbers are accurately represented.
    The survey's summaries rely on trust in the original papers, e.g., FID improvements attributed to MosaiQ and ReCon in Section 6.
  • domain assumption The taxonomy of fully quantum, hybrid, and quantum-assisted architectures is an exhaustive and useful partition of the field.
    The review organizes the entire field using this tripartite structure in Section 3, but some works, such as EQGAN with a classical discriminator, fit the categories only loosely.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A Survey of Quantum Generative Adversarial Networks: Architectures, Use Cases, and Real-World Implementations." pith.science (2026). https://pith.science/paper/KRU2P33C

@misc{pith2026250618002,
  author       = {Pith},
  title        = {Pith review of: A Survey of Quantum Generative Adversarial Networks: Architectures, Use Cases, and Real-World Implementations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KRU2P33C}},
  note         = {Machine review of arXiv:2506.18002}
}
read the original abstract

Quantum Generative Adversarial Networks (QGANs) have emerged as a promising direction in quantum machine learning, combining the strengths of quantum computing and adversarial training to enable efficient and expressive generative modeling. This survey provides a comprehensive overview of QGAN models, highlighting key advances from theoretical proposals to experimental realizations. We categorize existing QGAN architectures based on their quantum-classical hybrid structures and summarize their applications in fields such as image synthesis, medical data generation, channel prediction, software defect detection, and educational tools. Special attention is given to the integration of QGANs with domain-specific techniques, such as optimization heuristics, Wasserstein distance, variational circuits, and large language models. We also review experimental demonstrations on photonic and ion-trap quantum processors, assessing their feasibility under current hardware limitations. This survey aims to guide future research by outlining existing trends, challenges, and opportunities in developing QGANs for practical quantum advantage.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Towards quantum machine learning for assessing the resilience of post-quantum cryptography

    quant-ph 2026-07 conditional novelty 3.0 of 10

    A 16-qubit QGAN can approximate the first-byte distribution of SPHINCS+ signatures in simulation, but the result is a small-scale, unbenchmarked demonstration with no attack.

Reference graph

Works this paper leans on

89 extracted references · 33 canonical work pages · cited by 1 Pith paper

  1. [12]

    Quantum Science and Technology4(1), 014001 (2018) https://doi.org/10.1088/2058-9565/aada1f

    Khoshaman, A., Vinci, W., Denis, B., Andriyash, E., Sadeghi, H., Amin, M.H.: Quantum variational autoencoder. Quantum Science and Technology4(1), 014001 (2018) https://doi.org/10.1088/2058-9565/aada1f

    doi:10.1088/2058-9565/aada1f 2018

  2. [33]

    Nature574(7779), 505–510 (2019) https://doi.org/10.1038/s41586-019-1666-5

    Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J.C., Barends, R., Biswas, R., Boixo, S., Brandao, F.G., Buell, D.A.,et al.: Quantum supremacy using a programmable superconducting processor. Nature574(7779), 505–510 (2019) https://doi.org/10.1038/s41586-019-1666-5

    doi:10.1038/s41586-019-1666-5 2019

  3. [59]

    Electronics12(4), 856 (2023) https://doi.org/10

    Ngo, T.A., Nguyen, T., Thang, T.C.: A survey of recent advances in quantum generative adversarial networks. Electronics12(4), 856 (2023) https://doi.org/10. 3390/electronics12040856

    2023

  4. [85]

    arXiv preprint arXiv:2504.08728 (2025) https://doi.org/ 10.48550/arXiv.2504.08728

    Sekwao, S., Iaconis, J., Girotto, C., Roetteler, M., Kang, M., Kim, D., Noh, S., Kyoung, W., Shin, K.: End-to-end demonstration of quantum generative adversarial networks for steel microstructure image augmentation on a trapped- ion quantum computer. arXiv preprint arXiv:2504.08728 (2025) https://doi.org/ 10.48550/arXiv.2504.08728

    arXiv doi:10.48550/arxiv.2504.08728 2025

  5. [1]

    Advances in neural information processing systems27(2014) https://doi.org/10.48550/arXiv.1406

    Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. Advances in neural information processing systems27(2014) https://doi.org/10.48550/arXiv.1406. 2661

    doi:10.48550/arxiv.1406 2014

  6. [2]

    Cambridge University Press, ??? (2010) 21

    Nielsen, M.A., Chuang, I.L.: Quantum Computation and Quantum Information. Cambridge University Press, ??? (2010) 21

    2010

  7. [3]

    In: 2020 In- ternational Joint Conference on Beural Networks (IJCNN), pp

    Ramezani, S.B., Sommers, A., Manchukonda, H.K., Rahimi, S., Amirlatifi, A.: Machine learning algorithms in quantum computing: A survey. In: 2020 In- ternational Joint Conference on Beural Networks (IJCNN), pp. 1–8 (2020). https://doi.org/10.1109/IJCNN48605.2020.9207714 . IEEE

    work page arXiv 2020

  8. [5]

    Physical Review Letters121(4), 040502 (2018) https://doi.org/10.1103/PhysRevLett.121

    Lloyd, S., Weedbrook, C.: Quantum generative adversarial learning. Physical Review Letters121(4), 040502 (2018) https://doi.org/10.1103/PhysRevLett.121. 040502

    doi:10.1103/physrevlett.121 2018

Show all 89 references
  1. [6]

    Nature Communications5(1), 4213 (2014) https://doi.org/ 10.1038/ncomms5213

    Peruzzo, A., McClean, J., Shadbolt, P., Yung, M.-H., Zhou, X.-Q., Love, P.J., Aspuru-Guzik, A., O’brien, J.L.: A variational eigenvalue solver on a photonic quantum processor. Nature Communications5(1), 4213 (2014) https://doi.org/ 10.1038/ncomms5213

    2014 doi

  2. [7]

    arXiv preprint arXiv:1411.4028 (2014) https://doi.org/10.48550/arXiv

    Farhi, E., Goldstone, J., Gutmann, S.: A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028 (2014) https://doi.org/10.48550/arXiv. 1411.4028

    2014 doi arXiv

  3. [8]

    Quantum Science and Technology4(4), 043001 (2019) https://doi.org/10.1088/2058-9565/ab4eb5

    Benedetti, M., Lloyd, E., Sack, S., Fiorentini, M.: Parameterized quantum circuits as machine learning models. Quantum Science and Technology4(4), 043001 (2019) https://doi.org/10.1088/2058-9565/ab4eb5

    2019 doi

  4. [10]

    Physical Review A98(6), 062324 (2018) https://doi.org/10.1103/PhysRevA.98

    Liu, J.-G., Wang, L.: Differentiable learning of quantum circuit born machines. Physical Review A98(6), 062324 (2018) https://doi.org/10.1103/PhysRevA.98. 062324

    2018 doi

  5. [11]

    In: 2021 IEEE International Conference on Quantum Computing and Engineering (QCE), pp

    Stein, S.A., Baheri, B., Chen, D., Mao, Y., Guan, Q., Li, A., Fang, B., Xu, S.: Qugan: A quantum state fidelity based generative adversarial network. In: 2021 IEEE International Conference on Quantum Computing and Engineering (QCE), pp. 71–81 (2021). https://doi.org/10.1109/QC...

    2021

  6. [13]

    npj Quantum Information 4(1), 65 (2018) https://doi.org/10.1038/s41534-018-0116-9 22

    Grant, E., Benedetti, M., Cao, S., Hallam, A., Lockhart, J., Stojevic, V., Green, A.G., Severini, S.: Hierarchical quantum classifiers. npj Quantum Information 4(1), 65 (2018) https://doi.org/10.1038/s41534-018-0116-9 22

    2018 doi

  7. [14]

    Physical Review A99(3), 032331 (2019) https: //doi.org/10.1103/PhysRevA.99.032331

    Schuld, M., Bergholm, V., Gogolin, C., Izaac, J., Killoran, N.: Evaluating analytic gradients on quantum hardware. Physical Review A99(3), 032331 (2019) https: //doi.org/10.1103/PhysRevA.99.032331

    2019 doi

  8. [15]

    Physical Review E96(4), 043312 (2017) https: //doi.org/10.1103/PhysRevE.96.043312

    Karimi, H., Rosenberg, G., Katzgraber, H.G.: Effective optimization using sample persistence: A case study on quantum annealers and various monte carlo optimization methods. Physical Review E96(4), 043312 (2017) https: //doi.org/10.1103/PhysRevE.96.043312

    2017 doi

  9. [16]

    arXiv preprint arXiv:2003.02989 (2020) https://doi.org/10.48550/arXiv.2003.02989

    Broughton, M., Verdon, G., McCourt, T., Martinez, A.J., Yoo, J.H., Isakov, S.V., Massey, P., Halavati, R., Niu, M.Y., Zlokapa, A.,et al.: Tensorflow quantum: A soft- ware framework for quantum machine learning. arXiv preprint arXiv:2003.02989 (2020) https://doi.org/10.48550/ar...

    2020 doi arXiv

  10. [17]

    arXiv preprint arXiv:1907.05415 (2019) https://doi.org/ 10.48550/arXiv.1907.05415

    Verdon, G., Broughton, M., McClean, J.R., Sung, K.J., Babbush, R., Jiang, Z., Neven, H., Mohseni, M.: Learning to learn with quantum neural networks via classical neural networks. arXiv preprint arXiv:1907.05415 (2019) https://doi.org/ 10.48550/arXiv.1907.05415

    2019 doi arXiv

  11. [18]

    Nature Communications 9(1), 4812 (2018) https://doi.org/10.1038/s41467-018-07090-4

    McClean, J.R., Boixo, S., Smelyanskiy, V.N., Babbush, R., Neven, H.: Barren plateaus in quantum neural network training landscapes. Nature Communications 9(1), 4812 (2018) https://doi.org/10.1038/s41467-018-07090-4

    2018 doi

  12. [19]

    Nature Communications12(1), 1791 (2021) https://doi.org/10.1038/s41467-021-21728-w

    Cerezo, M., Sone, A., Volkoff, T., Cincio, L., Coles, P.J.: Cost function de- pendent Barren plateaus in shallow parametrized quantum circuits. Nature Communications12(1), 1791 (2021) https://doi.org/10.1038/s41467-021-21728-w

    2021 doi

  13. [20]

    Quantum Science and Technology6(3), 035006 (2021) https://doi.org/10.1088/2058-9565/abf51a

    Cerezo, M., Coles, P.J.: Higher order derivatives of quantum neural networks with Barren plateaus. Quantum Science and Technology6(3), 035006 (2021) https://doi.org/10.1088/2058-9565/abf51a

    2021 doi

  14. [21]

    Quantum 4, 269 (2020) https://doi.org/10.22331/q-2020-05-25-269

    Stokes, J., Izaac, J., Killoran, N., Carleo, G.: Quantum natural gradient. Quantum 4, 269 (2020) https://doi.org/10.22331/q-2020-05-25-269

    2020 doi

  15. [22]

    Scientific Reports4(1), 5422 (2014) https://doi.org/10.1038/srep05422

    Erol, V., Ozaydin, F., Altintas, A.A.: Analysis of entanglement measures and locc maximized quantum fisher information of general two qubit systems. Scientific Reports4(1), 5422 (2014) https://doi.org/10.1038/srep05422

    2014 doi

  16. [23]

    Scientific Reports5(1), 16360 (2015) https://doi.org/10.1038/srep16360

    Ozaydin, F., Altintas, A.A.: Quantum metrology: Surpassing the shot-noise limit with Dzyaloshinskii-Moriya interaction. Scientific Reports5(1), 16360 (2015) https://doi.org/10.1038/srep16360

    2015 doi

  17. [24]

    Physical Review Letters122(4), 040504 (2019) https://doi.org/10.1103/ PhysRevLett.122.040504

    Schuld, M., Killoran, N.: Quantum machine learning in feature Hilbert spaces. Physical Review Letters122(4), 040504 (2019) https://doi.org/10.1103/ PhysRevLett.122.040504

    2019

  18. [25]

    arXiv preprint arXiv:1802.06002 (2018) https://doi.org/10.48550/ arXiv.1802.06002

    Farhi, E., Neven, H.: Classification with quantum neural networks on near term 23 processors. arXiv preprint arXiv:1802.06002 (2018) https://doi.org/10.48550/ arXiv.1802.06002

    2018 doi arXiv

  19. [26]

    Physical Review Research1(3), 033063 (2019) https://doi.org/10.1103/PhysRevResearch.1.033063

    Killoran, N., Bromley, T.R., Arrazola, J.M., Schuld, M., Quesada, N., Lloyd, S.: Continuous-variable quantum neural networks. Physical Review Research1(3), 033063 (2019) https://doi.org/10.1103/PhysRevResearch.1.033063

    2019 doi

  20. [27]

    Physical Review Letters128(22), 220505 (2022) https://doi.org/10.1103/PhysRevLett.128.220505

    Niu, M.Y., Zlokapa, A., Broughton, M., Boixo, S., Mohseni, M., Smelyanskyi, V., Neven, H.: Entangling quantum generative adversarial networks. Physical Review Letters128(22), 220505 (2022) https://doi.org/10.1103/PhysRevLett.128.220505

    2022 doi

  21. [28]

    Science Advances5(1), 2761 (2019) https: //doi.org/10.1126/sciadv.aav2761

    Hu, L., Wu, S.-H., Cai, W., Ma, Y., Mu, X., Xu, Y., Wang, H., Song, Y., Deng, D.-L., Zou, C.-L.,et al.: Quantum generative adversarial learning in a superconducting quantum circuit. Science Advances5(1), 2761 (2019) https: //doi.org/10.1126/sciadv.aav2761

    2019 doi

  22. [29]

    Advanced Quantum Technologies4(1), 2000003 (2021) https://doi.org/10.1002/qute.202000003

    Romero, J., Aspuru-Guzik, A.: Variational quantum generators: Generative adver- sarial quantum machine learning for continuous distributions. Advanced Quantum Technologies4(1), 2000003 (2021) https://doi.org/10.1002/qute.202000003

    2021 doi

  23. [30]

    Nature567(7747), 209–212 (2019) https://doi.org/10.1038/s41586-019-0980-2

    Havl´ ıˇ cek, V., C´ orcoles, A.D., Temme, K., Harrow, A.W., Kandala, A., Chow, J.M., Gambetta, J.M.: Supervised learning with quantum-enhanced feature spaces. Nature567(7747), 209–212 (2019) https://doi.org/10.1038/s41586-019-0980-2

    2019 doi

  24. [31]

    Quantum Science and Technology3(3), 030502 (2018) https://doi

    Perdomo-Ortiz, A., Benedetti, M., Realpe-G´ omez, J., Biswas, R.: Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers. Quantum Science and Technology3(3), 030502 (2018) https://doi. org/10.1088/2058-9565/aab859

    2018 doi

  25. [32]

    Nature549(7671), 242–246 (2017) https://doi

    Kandala, A., Mezzacapo, A., Temme, K., Takita, M., Brink, M., Chow, J.M., Gambetta, J.M.: Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets. Nature549(7671), 242–246 (2017) https://doi. org/10.1038/nature23879

    2017 doi

  26. [34]

    IEEE Transactions on Quantum Engineering2, 1–8 (2021) https: //doi.org/10.1109/TQE.2021.3104804

    Li, J., Topaloglu, R.O., Ghosh, S.: Quantum generative models for small molecule drug discovery. IEEE Transactions on Quantum Engineering2, 1–8 (2021) https: //doi.org/10.1109/TQE.2021.3104804

    2021

  27. [35]

    Chemical Reviews119(19), 10856–10915 (2019) https://doi.org/10.1021/acs.chemrev.8b00803 24

    Cao, Y., Romero, J., Olson, J.P., Degroote, M., Johnson, P.D., Kieferov´ a, M., Kivlichan, I.D., Menke, T., Peropadre, B., Sawaya, N.P.,et al.: Quantum chemistry in the age of quantum computing. Chemical Reviews119(19), 10856–10915 (2019) https://doi.org/10.1021/acs.chemrev.8b00803 24

    2019 doi

  28. [36]

    Reviews of Modern Physics92(1), 015003 (2020) https: //doi.org/10.1103/RevModPhys.92.015003

    McArdle, S., Endo, S., Aspuru-Guzik, A., Benjamin, S.C., Yuan, X.: Quantum computational chemistry. Reviews of Modern Physics92(1), 015003 (2020) https: //doi.org/10.1103/RevModPhys.92.015003

    2020 doi

  29. [37]

    Chemical Reviews120(22), 12685–12717 (2020) https://doi.org/10.1021/acs.chemrev.9b00829

    Bauer, B., Bravyi, S., Motta, M., Chan, G.K.-L.: Quantum algorithms for quantum chemistry and quantum materials science. Chemical Reviews120(22), 12685–12717 (2020) https://doi.org/10.1021/acs.chemrev.9b00829

    2020 doi

  30. [38]

    arXiv preprint arXiv:2503.15403 (2025) https://doi.org/10.48550/arXiv.2503

    Choudhary, P.K., Innan, N., Shafique, M., Singh, R.: Hqnn-fsp: A hybrid classical- quantum neural network for regression-based financial stock market prediction. arXiv preprint arXiv:2503.15403 (2025) https://doi.org/10.48550/arXiv.2503. 15403

    2025 doi arXiv

  31. [39]

    In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp

    Araujo, R.d.A., Oliveira, A.L., Soares, S.C.: A quantum-inspired hybrid methodol- ogy for financial time series prediction. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2010). https://doi.org/10.1109/IJCNN. 2010.5604601 . IEEE

    2010

  32. [40]

    npj Quantum Information5(1), 103 (2019) https://doi.org/10.1038/s41534-019-0223-2

    Zoufal, C., Lucchi, A., Woerner, S.: Quantum generative adversarial networks for learning and loading random distributions. npj Quantum Information5(1), 103 (2019) https://doi.org/10.1038/s41534-019-0223-2

    2019 doi

  33. [41]

    Nature Reviews Physics3(9), 625–644 (2021) https://doi.org/10.1038/ s42254-021-00348-9

    Cerezo, M., Arrasmith, A., Babbush, R., Benjamin, S.C., Endo, S., Fujii, K., McClean, J.R., Mitarai, K., Yuan, X., Cincio, L.,et al.: Variational quantum algorithms. Nature Reviews Physics3(9), 625–644 (2021) https://doi.org/10.1038/ s42254-021-00348-9

    2021

  34. [42]

    Quantum2, 79 (2018) https://doi.org/10.22331/q-2018-08-06-79

    Preskill, J.: Quantum computing in the NISQ era and beyond. Quantum2, 79 (2018) https://doi.org/10.22331/q-2018-08-06-79

    2018 doi

  35. [43]

    arXiv preprint arXiv:2305.07284 (2023) https://doi.org/10.48550/arXiv.2305.07284

    Rehm, F., Vallecorsa, S., Grossi, M., Borras, K., Kr¨ ucker, D.: A full quantum generative adversarial network model for high energy physics simulations. arXiv preprint arXiv:2305.07284 (2023) https://doi.org/10.48550/arXiv.2305.07284

    2023 doi arXiv

  36. [44]

    In: 2023 IEEE International Conference on Quantum Computing and Engineering (QCE), vol

    Bermot, E., Zoufal, C., Grossi, M., Schuhmacher, J., Tacchino, F., Vallecorsa, S., Tavernelli, I.: Quantum generative adversarial networks for anomaly detection in high energy physics. In: 2023 IEEE International Conference on Quantum Computing and Engineering (QCE), vol. 1, p...

    2023

  37. [45]

    In: EPJ Web of Conferences, vol

    Chang, S.Y., Herbert, S., Vallecorsa, S., Combarro, E.F., Duncan, R.: Dual- parameterized quantum circuit gan model in high energy physics. In: EPJ Web of Conferences, vol. 251, p. 03050 (2021). https://doi.org/10.1051/epjconf/ 202125103050 . EDP Sciences

    2021 doi

  38. [46]

    Physical Review Letters 25 127(14), 140502 (2021) https://doi.org/10.1103/PhysRevLett.127.140502

    Ahmed, S., S´ anchez Mu˜ noz, C., Nori, F., Kockum, A.F.: Quantum state tomogra- phy with conditional generative adversarial networks. Physical Review Letters 25 127(14), 140502 (2021) https://doi.org/10.1103/PhysRevLett.127.140502

    2021 doi

  39. [47]

    In: Journal of Physics: Conference Series, vol

    Borras, K., Chang, S.Y., Funcke, L., Grossi, M., Hartung, T., Jansen, K., Kruecker, D., K¨ uhn, S., Rehm, F., T¨ uys¨ uz, C.,et al.: Impact of quantum noise on the training of quantum generative adversarial networks. In: Journal of Physics: Conference Series, vol. 2438, p. 012...

    2023 doi

  40. [48]

    Journal of the Physical Society of Japan90(3), 032001 (2021) https://doi.org/10.7566/JPSJ.90.032001

    Endo, S., Cai, Z., Benjamin, S.C., Yuan, X.: Hybrid quantum-classical algorithms and quantum error mitigation. Journal of the Physical Society of Japan90(3), 032001 (2021) https://doi.org/10.7566/JPSJ.90.032001

    2021 doi

  41. [49]

    Physical Review Letters119(18), 180509 (2017) https://doi.org/10.1103/ PhysRevLett.119.180509

    Temme, K., Bravyi, S., Gambetta, J.M.: Error mitigation for short-depth quantum circuits. Physical Review Letters119(18), 180509 (2017) https://doi.org/10.1103/ PhysRevLett.119.180509

    2017

  42. [50]

    Advanced Quantum Technologies4(5), 2000069 (2021) https://doi.org/10.1002/qute.202000069

    Anand, A., Romero, J., Degroote, M., Aspuru-Guzik, A.: Noise robustness and experimental demonstration of a quantum generative adversarial network for continuous distributions. Advanced Quantum Technologies4(5), 2000069 (2021) https://doi.org/10.1002/qute.202000069

    2021 doi

  43. [51]

    New Journal of Physics22(4), 043006 (2020) https://doi

    Sharma, K., Khatri, S., Cerezo, M., Coles, P.J.: Noise resilience of variational quantum compiling. New Journal of Physics22(4), 043006 (2020) https://doi. org/10.1088/1367-2630/ab784c

    2020 doi

  44. [52]

    Reviews of Modern Physics94(1), 015004 (2022) https://doi.org/10.1103/RevModPhys.94.015004

    Bharti, K., Cervera-Lierta, A., Kyaw, T.H., Haug, T., Alperin-Lea, S., Anand, A., Degroote, M., Heimonen, H., Kottmann, J.S., Menke, T.,et al.: Noisy intermediate- scale quantum algorithms. Reviews of Modern Physics94(1), 015004 (2022) https://doi.org/10.1103/RevModPhys.94.015004

    2022 doi

  45. [53]

    Quantum5, 558 (2021) https://doi.org/ 10.22331/q-2021-10-05-558

    Arrasmith, A., Cerezo, M., Czarnik, P., Cincio, L., Coles, P.J.: Effect of Barren plateaus on gradient-free optimization. Quantum5, 558 (2021) https://doi.org/ 10.22331/q-2021-10-05-558

    2021 doi

  46. [54]

    Physical Review Letters 126(14), 140502 (2021) https://doi.org/10.1103/PhysRevLett.126.140502

    Harrow, A.W., Napp, J.C.: Low-depth gradient measurements can improve conver- gence in variational hybrid quantum-classical algorithms. Physical Review Letters 126(14), 140502 (2021) https://doi.org/10.1103/PhysRevLett.126.140502

    2021 doi

  47. [55]

    Physical Review A77, 062339 (2008) https://doi.org/10.1103/ PhysRevA.77.062339

    Wang, X.-B., You, J.Q., Nori, F.: Quantum entanglement via two-qubit quantum Zeno dynamics. Physical Review A77, 062339 (2008) https://doi.org/10.1103/ PhysRevA.77.062339

    2008

  48. [56]

    Scientific Reports12(1), 15302 (2022) https://doi.org/10.1038/s41598-022-19170-z

    Bayrakci, V., Ozaydin, F.: Quantum Zeno repeaters. Scientific Reports12(1), 15302 (2022) https://doi.org/10.1038/s41598-022-19170-z

    2022 doi

  49. [57]

    Physical Review A105(2), 26 022439 (2022) https://doi.org/10.1103/PhysRevA.105.022439

    Ozaydin, F., Bayindir, C., Altintas, A.A., Yesilyurt, C.: Nonlocal activation of bound entanglement via local quantum Zeno dynamics. Physical Review A105(2), 26 022439 (2022) https://doi.org/10.1103/PhysRevA.105.022439

    2022 doi

  50. [58]

    Applied Sciences13(2), 791 (2023) https://doi.org/10.3390/app13020791

    Ozaydin, F., Bayrakci, V., Altintas, A.A., Bayindir, C.: Superactivating bound entanglement in quantum networks via quantum Zeno dynamics and a novel algorithm for optimized Zeno evolution. Applied Sciences13(2), 791 (2023) https://doi.org/10.3390/app13020791

    2023 doi

  51. [60]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

    Silver, D., Patel, T., Cutler, W., Ranjan, A., Gandhi, H., Tiwari, D.: MosaiQ: Quantum generative adversarial networks for image generation on NISQ computers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7030–7039 (2023). https://doi.org/10....

    2023

  52. [61]

    In: 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), pp

    Rahman, M.A., Shahriar, H., Clincy, V., Hossain, M.F., Rahman, M.: A quantum generative adversarial network-based intrusion detection system. In: 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), pp. 1810–1815 (2023). https://doi.org/10.1109/COM...

    2023

  53. [62]

    Journal of Chemical Information and Modeling63(11), 3307–3318 (2023) https: //doi.org/10.1021/acs.jcim.3c00562

    Kao, P.-Y., Yang, Y.-C., Chiang, W.-Y., Hsiao, J.-Y., Cao, Y., Aliper, A., Ren, F., Aspuru-Guzik, A., Zhavoronkov, A., Hsieh, M.-H.,et al.: Exploring the advantages of quantum generative adversarial networks in generative chemistry. Journal of Chemical Information and Modeling...

    2023 doi

  54. [63]

    Machine Learning: Science and Technology4(2), 025023 (2023) https://doi.org/10.1088/2632-2153/acd6d8

    Pantis-Simut, C.-A., Preda, A.T., Ion, L., Manolescu, A., Nemnes, G.A.: Mapping confinement potentials and charge densities of interacting quantum systems using conditional generative adversarial networks. Machine Learning: Science and Technology4(2), 025023 (2023) https://doi...

    2023 doi

  55. [64]

    Quantum Science and Technology8(3), 035002 (2023) https://doi.org/10.1088/ 2058-9565/acc4e4

    Chaudhary, S., Huembeli, P., MacCormack, I., Patti, T.L., Kossaifi, J., Galda, A.: Towards a scalable discrete quantum generative adversarial neural network. Quantum Science and Technology8(3), 035002 (2023) https://doi.org/10.1088/ 2058-9565/acc4e4

    2023

  56. [65]

    New Journal of Physics25(8), 083019 (2023) https://doi.org/10.1088/1367-2630/ace8b4

    Huang, C., Zhang, S.: Enhancing adversarial robustness of quantum neural net- works by adding noise layers. New Journal of Physics25(8), 083019 (2023) https://doi.org/10.1088/1367-2630/ace8b4

    2023 doi

  57. [66]

    Computers in Biology and Medicine166, 107549 (2023) https://doi.org/10.1016/j.compbiomed

    Qu, Z., Shi, W., Tiwari, P.: Quantum conditional generative adversarial network based on patch method for abnormal electrocardiogram generation. Computers in Biology and Medicine166, 107549 (2023) https://doi.org/10.1016/j.compbiomed. 2023.107549

    2023

  58. [67]

    Physical Review Research6(3), 033019 (2024) https://doi.org/10.1103/ PhysRevResearch.6.033019

    Kim, L., Lloyd, S., Marvian, M.: Hamiltonian quantum generative adversarial 27 networks. Physical Review Research6(3), 033019 (2024) https://doi.org/10.1103/ PhysRevResearch.6.033019

    2024

  59. [68]

    In: Proceedings of the 43rd IEEE/ACM International Conference on Computer-Aided Design, pp

    DiBrita, N.S., Leeds, D., Huo, Y., Ludmir, J., Patel, T.: ReCon: Reconfiguring analog Rydberg atom quantum computers for quantum generative adversarial networks. In: Proceedings of the 43rd IEEE/ACM International Conference on Computer-Aided Design, pp. 1–9 (2024). https://doi...

    2024 doi

  60. [69]

    Chinese Physics B33(4), 040304 (2024) https://doi.org/10

    Zhao, R.-S., Ma, H.-Y., Cheng, T., Wang, S., Fan, X.-K.: Quantum generative adversarial networks based on a readout error mitigation method with fault tolerant mechanism. Chinese Physics B33(4), 040304 (2024) https://doi.org/10. 1088/1674-1056/ad02e7

    2024

  61. [70]

    Advanced Quantum Technologies, 2400171 (2024) https: //doi.org/10.1002/qute.202400171

    Ma, H., Ye, L., Guo, X., Ruan, F., Zhao, Z., Li, M., Wang, Y., Yang, J.: Quantum generative adversarial networks in a silicon photonic chip with max- imum expressibility. Advanced Quantum Technologies, 2400171 (2024) https: //doi.org/10.1002/qute.202400171

    2024 doi

  62. [71]

    Optica Quantum2(6), 458–467 (2024) https://doi.org/10.1364/ OPTICAQ.530346

    Sedrakyan, T., Salavrakos, A.: Photonic quantum generative adversarial networks for classical data. Optica Quantum2(6), 458–467 (2024) https://doi.org/10.1364/ OPTICAQ.530346

    2024

  63. [72]

    Quantum Machine Intelligence6(2), 84 (2024) https://doi.org/10.1007/ s42484-024-00220-w

    Xie, J., Liu, C., Dong, Y.: An evolutionary quantum generative adversarial network. Quantum Machine Intelligence6(2), 84 (2024) https://doi.org/10.1007/ s42484-024-00220-w

    2024

  64. [73]

    In: Proceedings of the 2024 8th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, pp

    Nokhwal, S., Nokhwal, S., Pahune, S., Chaudhary, A.: Quantum generative adversarial networks: Bridging classical and quantum realms. In: Proceedings of the 2024 8th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, pp. 105–109 (2024). https:...

    2024

  65. [74]

    Laser Physics Letters21(12), 125207 (2024) https://doi.org/10.1088/ 1612-202X/ad8742

    Gong, C., Wen, Z.-Y., Deng, Y.-W., Zhou, N.-R., Zeng, Q.-W.: Unrolled generative adversarial network for continuous distributions under hybrid quantum-classical model. Laser Physics Letters21(12), 125207 (2024) https://doi.org/10.1088/ 1612-202X/ad8742

    2024

  66. [75]

    In: 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), vol

    Cui, M., Chang, L., Chau, A., Mekuria, H., Adwankar, L., Pendyala, S., McMahan, L.: Efficient and optimized small organic molecular graph generation pathway using a quantum generative adversarial network. In: 2024 IEEE International Conference on Quantum Computing and Engineer...

    2024

  67. [76]

    IEEE Transactions on Quantum Engineering, 2500514 (2024) https://doi.org/10.1109/TQE.2024.3414264 28

    Anoshin, M., Sagingalieva, A., Mansell, C., Zhiganov, D., Shete, V., Pflitsch, M., Melnikov, A.: Hybrid quantum cycle generative adversarial network for small molecule generation. IEEE Transactions on Quantum Engineering, 2500514 (2024) https://doi.org/10.1109/TQE.2024.3414264 28

    2024

  68. [77]

    IEEE Access, 102688–102701 (2024) https://doi.org/10.1109/ACCESS.2024.3433383

    Boyle, A.O., Nikandish, R.: A hybrid quantum-classical generative adversarial network for near-term quantum processors. IEEE Access, 102688–102701 (2024) https://doi.org/10.1109/ACCESS.2024.3433383

    2024

  69. [78]

    Neurocomputing577, 127346 (2024) https://doi.org/10.1016/j.neucom.2024.127346

    Pu, Z., Koutti, L., Masmoudi, L., Oliveira, J.V.,et al.: A super resolution method based on generative adversarial networks with quantum feature enhancement: Application to aerial agricultural images. Neurocomputing577, 127346 (2024) https://doi.org/10.1016/j.neucom.2024.127346

    2024

  70. [79]

    Sensors23(7), 3765 (2023) https://doi.org/10.3390/s23073765

    Qin, M., Fan, Y., Guo, H., Zhang, L.: Laser-visible face image translation and recognition based on cyclegan and spectral normalization. Sensors23(7), 3765 (2023) https://doi.org/10.3390/s23073765

    2023 doi

  71. [80]

    Electronics13(11), 2158 (2024) https://doi.org/10.3390/ electronics13112158

    Orlandi, F., Barbierato, E., Gatti, A.: Enhancing financial time series prediction with quantum-enhanced synthetic data generation: A case study on the s&p 500 using a quantum wasserstein generative adversarial network approach with a gradient penalty. Electronics13(11), 2158 ...

    2024

  72. [81]

    Energy Conversion and Economics5(4), 193–210 (2024) https://doi.org/10.1049/enc2.12122

    Zhou, X., Zhao, H., Cao, Y., Fei, X., Liang, G., Zhao, J.: Carbon market risk estimation using quantum conditional generative adversarial network and am- plitude estimation. Energy Conversion and Economics5(4), 193–210 (2024) https://doi.org/10.1049/enc2.12122

    2024 doi

  73. [82]

    Knowledge-Based Systems301, 112260 (2024) https://doi.org/10.1016/j.knosys.2024.112260

    Qu, Z., Chen, W., Tiwari, P.: HQ-DCGAN: Hybrid quantum deep convolutional generative adversarial network approach for ECG generation. Knowledge-Based Systems301, 112260 (2024) https://doi.org/10.1016/j.knosys.2024.112260

    2024

  74. [83]

    Transactions on Emerging Telecommunications Technologies36(4), 70120 (2025) https://doi.org/10.1002/ett.70120

    Selvam, P.S., Begum, S.S., Pingle, Y., Srinivasan, S.: Optimized self-guided quantum generative adversarial network based scheduling framework for efficient resource utilization in cloud computing to enhance performance and reliability. Transactions on Emerging Telecommunicati...

    2025 doi

  75. [84]

    arXiv preprint arXiv:2503.12884 (2025) https://doi.org/10.48550/arXiv.2503.12884

    Ueda, K., Matsuo, A.: Optimizing ansatz design in quantum generative adversarial networks using large language models. arXiv preprint arXiv:2503.12884 (2025) https://doi.org/10.48550/arXiv.2503.12884

    2025 doi arXiv

  76. [86]

    IEEE Transactions on Geoscience and Remote Sensing, 5512119 (2025) https://doi.org/10.1109/TGRS.2025.3561951 29

    Lin, C.-H., Young, S.-S.: Hyperking: Quantum-classical generative adversarial net- works for hyperspectral image restoration. IEEE Transactions on Geoscience and Remote Sensing, 5512119 (2025) https://doi.org/10.1109/TGRS.2025.3561951 29

    2025

  77. [87]

    International Journal of Communication Systems38(8), 70086 (2025) https://doi.org/10.1002/ dac.70086

    Vijayakumari, P., Raja, M., Rahamtula, S., Lakshmi, P.S., Saikumar, P.J.: Hybrid quantum deep convolutional generative adversarial networks for channel prediction and performance enhancement in large-scale mimo-ofdm systems. International Journal of Communication Systems38(8),...

    2025

  78. [88]

    In: 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), pp

    Sriraksha, P., Vishal, R.K., Kulkarni, P.S.,et al.: Quantales: Bridging realms with quantum generative adversarial networks and transformers in educational content creation. In: 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical a...

    2025

  79. [89]

    Machine Learning: Science and Technology6(2), 025032 (2025) https://doi.org/10.1088/2632-2153/add1a9

    Khatun, A., Aydeniz, K.Y., Weinstein, Y.S., Usman, M.: Quantum generative learning for high-resolution medical image generation. Machine Learning: Science and Technology6(2), 025032 (2025) https://doi.org/10.1088/2632-2153/add1a9

    2025 doi

  80. [90]

    In: 2025 International Conference on Inventive Computation Technologies (ICICT), pp

    Chaudhary, J., AV, V.K., Sapatnekar, A., Barve, A., Maranan, R.,et al.: Enhanced software defect prediction using quantum hamiltonian generative adversarial network for improved software performance reliability. In: 2025 International Conference on Inventive Computation Techno...

    2025

  81. [91]

    Chintalapati, A., Enkhbat, K., Annamalai, R., Amali, G.B., Ozaydin, F., Noel, M.M.: Quantum-enhanced algorithmic fairness and the advancement of ai integrity and responsibility (2024) https://doi.org/10.20944/preprints202409.1749.v1 30

    2024

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.