ReCon: Reconfiguring Analog Rydberg Atom Quantum Computers for Quantum Generative Adversarial Networks

Autor:	DiBrita, Nicholas S., Leeds, Daniel, Huo, Yuqian, Ludmir, Jason, Patel, Tirthak
Rok vydání:	2024
Předmět:	Quantum Physics Computer Science - Computer Vision and Pattern Recognition Computer Science - Emerging Technologies
Druh dokumentu:	Working Paper
DOI:	10.1145/3676536.3676697
Popis:	Quantum computing has shown theoretical promise of speedup in several machine learning tasks, including generative tasks using generative adversarial networks (GANs). While quantum computers have been implemented with different types of technologies, recently, analog Rydberg atom quantum computers have been demonstrated to have desirable properties such as reconfigurable qubit (quantum bit) positions and multi-qubit operations. To leverage the properties of this technology, we propose ReCon, the first work to implement quantum GANs on analog Rydberg atom quantum computers. Our evaluation using simulations and real-computer executions shows 33% better quality (measured using Frechet Inception Distance (FID)) in generated images than the state-of-the-art technique implemented on superconducting-qubit technology. Comment: ReCon will appear in the Proceedings of the International Conference on Computer-Aided Design (ICCAD), 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2408.13389 Zobrazit plný text záznamu View this record from Arxiv