量子生成模型中的可训练性障碍与机遇（英文版）

英文标题：Trainability barriers and opportunities in quantum generative modeling中文摘要：本文研究了量子生成模型中如何解决靠近平原以及指数损失集中的挑战，提出了解决这个问题的新方法 Quantum fidelity-type loss，并且通过对高能物理数据建模表明了本研究理论结果的正确性。英文摘要：Quantum generative models, in providing inherently efficient samplingstrategies, show promise for achieving a near-term advantage on quantumhardware. Nonetheless, important questions remain regarding their scalability.In this work, we investigate the barriers to the trainability of quantumgenerative models posed