ReCon: Reconfiguring Analog Rydberg Atom Quantum Computers for Quantum Generative Adversarial Networks
This work addresses the challenge of leveraging reconfigurable quantum hardware for generative tasks, representing an incremental advancement in quantum machine learning.
The authors tackled the problem of implementing quantum generative adversarial networks (GANs) on analog Rydberg atom quantum computers, achieving a 33% improvement in image quality measured by Frechet Inception Distance compared to state-of-the-art methods on superconducting-qubit technology.
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.