mlsanityData to 14 Oct 2026

Hybrid quantum cycle generative adversarial network for small molecule generation

arXiv 2402.00014 ↗First posted 28 Dec 2023q-bio.BM cs.ET cs.LGcited by 8

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Abstract (arXiv)

The drug design process currently requires considerable time and resources to develop each new compound that enters the market. This work develops an application of hybrid quantum generative models based on the integration of parametrized quantum circuits into known molecular generative adversarial networks, and proposes quantum cycle architectures that improve model performance and stability during training. Through extensive experimentation on benchmark drug design datasets, QM9 and PC9, the introduced models are shown to outperform the previously achieved scores. Most prominently, the new scores indicate an increase of up to 30% in the quantitative estimation of druglikeness. The new hybrid quantum machine learning algorithms, as well as the achieved scores of pharmacokinetic properties, contribute to the development of fast and accurate drug discovery processes.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 26 of its 63 references that are in the map. First posted on arXiv 28 Dec 2023.

Builds on

26 of 63 references.

References · 26

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14 more
  1. 2022Hybrid quantum neural network for drug response predictioncited by 26
  2. 2022Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generationcited by 22
  3. 2022High Dimensional Quantum Machine Learning With Small Quantum Computerscited by 18
  4. 2022Benchmarking simulated and physical quantum processing units using quantum and hybrid algorithmscited by 18
  5. 2023Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapescited by 18
  6. 2022Hybrid quantum ResNet for car classification and its hyperparameter optimizationcited by 17
  7. 2022An exponentially-growing family of universal quantum circuitscited by 17
  8. 2023Quantum algorithms applied to satellite mission planning for Earth observationcited by 17
  9. 2022Practical application-specific advantage through hybrid quantum computingcited by 16
  10. 2023Hybrid quantum image classification and federated learning for hepatic steatosis diagnosiscited by 15
  11. 2023Parallel Hybrid Networks: an interplay between quantum and classical neural networkscited by 14
  12. 2021Hybrid quantum-classical machine learning for generative chemistry and drug designcited by 10
  13. 2023A supervised hybrid quantum machine learning solution to the emergency escape routing problemcited by 7
  14. 2023Photovoltaic power forecasting using quantum machine learningcited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 3.

8 papers.

  1. 2024Method for noise-induced regularization in quantum neural networkscited by 7
  2. 2025Predictive control of blast furnace temperature in steelmaking with hybrid depth-infused quantum neural networkscited by 2
  3. 2026Exponential capacity scaling of classical GANs compared to hybrid latent style-based quantum GANscited by 2
  4. 2025Bridging Quantum and Classical Computing in Drug Design: Architecture Principles for Improved Molecule Generationcited by 1
  5. 2025QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learningcited by 1
  6. 2025Hybrid quantum recurrent neural network for remaining useful life prediction of turbofan enginescited by 0
  7. 2026Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentialscited by 0
  8. 2026Latent Style-based Quantum Wasserstein GAN for Drug Designcited by 0