Cerezo M, Larocca Martin, García-Martín Diego, Diaz N L, Braccia Paolo, Fontana Enrico, Rudolph Manuel S, Bermejo Pablo, Ijaz Aroosa, Thanasilp Supanut, Anschuetz Eric R, Holmes Zoë
Information Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA.
Quantum Science Center, Oak Ridge, TN, USA.
Nat Commun. 2025 Aug 25;16(1):7907. doi: 10.1038/s41467-025-63099-6.
A large amount of effort has recently been put into understanding the barren plateau phenomenon. In this perspective article, we face the increasingly loud elephant in the room and ask a question that has been hinted at by many but not explicitly addressed: Can the structure that allows one to avoid barren plateaus also be leveraged to efficiently simulate the loss classically? We collect evidence-on a case-by-case basis-that many commonly used models whose loss landscapes avoid barren plateaus can also admit classical simulation, provided that one can collect some classical data from quantum devices during an initial data acquisition phase. This follows from the observation that barren plateaus result from a curse of dimensionality, and that current approaches for solving them end up encoding the problem into some small, classically simulable, subspaces. Thus, while stressing that quantum computers can be essential for collecting data, our analysis sheds doubt on the information processing capabilities of many parametrized quantum circuits with provably barren plateau-free landscapes. We end by discussing the (many) caveats in our arguments including the limitations of average case arguments, the role of smart initializations, models that fall outside our assumptions, the potential for provably superpolynomial advantages and the possibility that, once larger devices become available, parametrized quantum circuits could heuristically outperform our analytic expectations.
最近，人们投入了大量精力来理解贫瘠高原现象。在这篇观点文章中，我们直面房间里这头越来越引人注目的“大象”，并提出一个许多人都曾暗示过但未明确提及的问题：能够避免贫瘠高原的结构是否也可用于有效地经典模拟损失？我们逐案收集证据表明，许多常用的损失景观避免了贫瘠高原的模型也可以进行经典模拟，前提是在初始数据采集阶段能够从量子设备收集一些经典数据。这源于这样的观察，即贫瘠高原是由维度诅咒导致的，并且当前解决它们的方法最终会将问题编码到一些小的、可经典模拟的子空间中。因此，虽然强调量子计算机对于收集数据至关重要，但我们的分析对许多具有可证明无贫瘠高原景观的参数化量子电路的信息处理能力提出了质疑。最后，我们讨论了论证中的（诸多）注意事项，包括平均情况论证的局限性、智能初始化的作用、超出我们假设的模型、可证明的超多项式优势的可能性，以及一旦有更大的设备可用，参数化量子电路可能在启发式上优于我们分析预期的可能性。
