研究条件量子生成对抗网络算法在具有误差抑制的量子硬件上进行分布加载的性能
Investigate the Performance of Distribution Loading with Conditional Quantum Generative Adversarial Network Algorithm on Quantum Hardware with Error Suppression
AI总结：
本文评估 Fire Opal 误差抑制与 AI 电路优化结合 IBM 量子硬件对条件量子 GAN 多模态分布加载的提升，并综述多种误差缓解策略。
摘要：
本研究考察了与 IBM 量子计算平台集成的 Fire Opal 误差抑制和 AI 电路优化系统在多模态分布加载算法中的效能。采用 Kullback-Leibler（KL）散度作为定量误差分析，结果表明，与模拟器上的结果相比，Fire Opal 可将我们的条件量子生成对抗算法生成的含时分布改善 30–40%。此外，尽管需要运行更多试验，Fire Opal 在复杂电路上的性能仍保持稳定。研究得出结论：Fire Opal 的误差抑制和电路优化显著增强了量子计算流程，突显了其在实际应用中的潜力。此外，本研究还综述了主要的误差缓解策略，包括零噪声外推（ZNE）、概率误差消除（PEC）、Pauli 旋转、测量误差缓解和机器学习方法，并从技术实现、量子资源和可扩展性方面评估了它们的优缺点。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
