Investigate the Performance of Distribution Loading with Conditional Quantum Generative Adversarial Network Algorithm on Quantum Hardware with Error Suppression

TL;DR

This paper evaluates the effectiveness of Fire Opal error suppression combined with a Conditional Quantum GAN on IBM quantum hardware, demonstrating significant improvements in distribution loading accuracy and robustness against complex circuits.

Contribution

It introduces the integration of Fire Opal error suppression with a Conditional Quantum GAN, showing enhanced performance on real quantum hardware compared to simulations.

Findings

Fire Opal improves distribution accuracy by 30-40% on hardware.

Performance remains stable for complex circuits despite increased trials.

Error suppression significantly enhances practical quantum computing applications.

Abstract

The study examines the efficacy of the Fire Opal error suppression and AI circuit optimization system integrated with IBM's quantum computing platform for a multi-modal distribution loading algorithm. Using Kullback-Leibler (KL) divergence as a quantitative error analysis, the results indicate that Fire Opal can improve on the time-dependent distributions generated by our Conditional Quantum Generative Adversarial algorithm by 30-40\% in comparison with the results on the simulator. In addition, Fire Opal's performance remains consistent for complex circuits despite the needs to run more trials. The research concludes that Fire Opal's error suppression and circuit optimization significantly enhanced quantum computing processes, highlighting its potential for practical applications. In addition, the study also reviews leading error mitigation strategies, including zero noise extrapolation (ZNE), probabilistic error cancellation (PEC), Pauli twirling, measurement error mitigation, and machine learning methods, assessing their advantages and disadvantages in terms of technical implementation, quantum resources, and scalability.