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

TL;DR

This paper introduces ReCon, a novel approach to implement quantum generative adversarial networks on analog Rydberg atom quantum computers, demonstrating improved image generation quality over existing superconducting-qubit methods.

Contribution

ReCon is the first method to realize quantum GANs on analog Rydberg atom quantum computers, leveraging their reconfigurable qubits and multi-qubit operations.

Findings

33% better FID score in generated images

Successful implementation on both simulations and real hardware

Outperforms superconducting-qubit based quantum GANs

Abstract

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.