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MosaiQ: Quantum Generative Adversarial Networks for Image Generation on NISQ Computers
arXiv
Authors: Daniel Silver, Tirthak Patel, William Cutler, Aditya Ranjan, Harshitta Gandhi, Devesh Tiwari
Year
2023
Paper ID
55605
Status
Preprint
Abstract Read
~2 min
Abstract Words
73
Citations
N/A
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.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2023 reference point for readers tracking recent quantum research.
- Quantum machine learning and vision have come to the fore recently, with hardware advances enabling rapid advancement in the capabilities of quantum machines.
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