mlsanityData to 14 Oct 2026

OrganiQ: Mitigating Classical Resource Bottlenecks of Quantum Generative Adversarial Networks on NISQ-Era Machines

arXiv 2409.19823 ↗First posted 29 Sep 2024quant-ph cs.AIcited by 0

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Abstract (arXiv)

Driven by swift progress in hardware capabilities, quantum machine learning has emerged as a research area of interest. Recently, quantum image generation has produced promising results. However, prior quantum image generation techniques rely on classical neural networks, limiting their quantum potential and image quality. To overcome this, we introduce OrganiQ, the first quantum GAN capable of producing high-quality images without using classical neural networks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 31 references that are in the map. First posted on arXiv 29 Sep 2024.

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References · 17

  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  2. 2022Generative Adversarial Networkscited by 5,627
  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  4. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,988
  5. 2020Variational Quantum Algorithmscited by 692
  6. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 167
  7. 2018Quantum generative adversarial networkscited by 128
  8. 2021Pros and Cons of GAN Evaluation Measures: New Developmentscited by 123
  9. 2021QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuitscited by 98
  10. 2022Recent Advances for Quantum Neural Networks in Generative Learningcited by 39
  11. 2022Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generationcited by 22
  12. 2023MosaiQ: Quantum Generative Adversarial Networks for Image Generation on NISQ Computerscited by 16
5 more
  1. 2019Adversarial Generation of Time-Frequency Features with application in audio synthesiscited by 13
  2. 2023QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifierscited by 11
  3. 2018Quantum classification of the MNIST dataset with Slow Feature Analysiscited by 5
  4. 2023Toward Privacy in Quantum Program Execution On Untrusted Quantum Cloud Computing Machines for Business-sensitive Quantum Needscited by 4
  5. 2023SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computerscited by 3

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