永恒虫洞与噪声Sycamore的无尽故事
The Neverending Story of the Eternal Wormhole and the Noisy Sycamore
AI总结：
本文回顾并分析了Jafferis等人利用Sycamore量子处理器模拟可穿越虫洞的实验，探讨了Yao等人对该学习哈密顿量提出的缺陷及团队的回应，并从哲学角度进行了评论。
摘要：
Daniel Jafferis等人最新的Nature论文《量子处理器上的可穿越虫洞动力学》引发了广泛关注。该Nature论文讨论了一项实验，其中使用Google的Sycamore量子处理器模拟了一个包含5项（一个学习哈密顿量）的稀疏N = 7 SYK模型。该Nature论文表明，学习哈密顿量保留了包含210项的N = 10 SYK模型的关键引力特征，并足以产生可穿越虫洞行为。为纪念Ian Hacking，我将检查该实验并讨论有关该实验的一些哲学挑战。最近，Norman Yao和两名研究生发现了Jafferis等人学习哈密顿量中的多个缺陷，并在该Nature论文上上传了一条评论。正如预期的那样，Jafferis及其团队找到了一种简单的方法来澄清这一误解。他们找到了一种物理上的理由，使他们得以避免该问题。在本文中，我阐明了Yao及其学生提出的主要论点，以及Jafferis等人拯救其学习哈密顿量的方法。我将以在学习哈密顿量背景下对这一近期发展的哲学评论结束本文。
英文摘要：
There has been a great buzz surrounding Daniel Jafferis et al.'s latest Nature paper, "Traversable wormhole dynamics on a quantum processor". The Nature paper discusses an experiment in which Google's Sycamore quantum processor is used to simulate a sparse N = 7 SYK model with 5 terms (a learned Hamiltonian). The Nature paper shows that the learned Hamiltonian preserves the key gravitational characteristics of an N = 10 SYK model with 210 terms and is sufficient to produce a traversable wormhole behavior. I will examine the experiment and discuss some philosophical challenges concerning the experiment in memory of Ian Hacking. Recently, Norman Yao and two graduate students discovered multiple flaws in Jafferis et al.'s learned Hamiltonian and uploaded a comment on the Nature paper. As expected, Jafferis and his team found a simple way to clarify the misunderstanding. They found a physical justification that allowed them to avoid the problem. In this paper, I elucidate the main arguments Yao and his students raised and the way Jafferis et al. found to save their learned Hamiltonian. I will end this paper with a philosophical comment on this recent development in the context of the learned Hamiltonian.
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。
