  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.
