OrganiQ: Mitigating Classical Resource Bottlenecks of Quantum Generative Adversarial Networks on NISQ-Era Machines
<|reference_start|>OrganiQ: Mitigating Classical Resource Bottlenecks of Quantum Generative Adversarial Networks on NISQ-Era Machines: 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.<|reference_end|>
@article{silver2024organiq:,
    title={OrganiQ: Mitigating Classical Resource Bottlenecks of Quantum Generative
  Adversarial Networks on NISQ-Era Machines},
    author={Daniel Silver, Tirthak Patel, Aditya Ranjan, William Cutler, Devesh
  Tiwari},
    journal={arXiv preprint arXiv:2409.19823},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19823},
    primaryClass={quant-ph cs.AI}
}