MosaiQ: Quantum Generative Adversarial Networks for Image Generation on NISQ Computers
Daniel Silver, Aditya Ranjan, Tirthak Patel, Harshitta Gandhi, William Cutler, Devesh Tiwari
Abstract
Quantum machine learning and vision have come to the fore recently, with hardware advances enabling rapid advancement in the capabilities of quantum machines. Recently, quantum image generation has been explored with many potential advantages over non-quantum techniques; however, previous techniques have suffered from poor quality and robustness. To address these problems, we introduce MosaiQ a high-quality quantum image generation GAN framework that can be executed on today's Near-term Intermediate Scale Quantum (NISQ) computers. 1
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Cited by top-tier papers2
- EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical DataJason Han, Nicholas S. DiBrita, Younghyun Cho, Hengrui Luo et al.DAC 2025 · 2 citations
- ResQ: A Novel Framework to Implement Residual Neural Networks on Analog Rydberg Atom Quantum ComputersNicholas S. DiBrita, Jason Han, Tirthak PatelICCV 2025
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