论文标题
神经量子状态可以学习体积法基态吗？
Can neural quantum states learn volume-law ground states?
论文作者
论文摘要
我们研究基于多层进料前馈网络的神经量子状态是否可以找到具有体积法律纠缠熵的基态。作为测试台，我们采用了范式的sachdev-ye-kitaev模型。我们发现，浅层和深馈网络都需要指数级的参数，以代表该模型的基础状态。这表明，尽管是对相关模型而非病理病例的物理解决方案，但仍然很难学会到更大的系统大小的棘手性。这突出了对有效神经表示的量子状态的物理特性的进一步研究的重要性。
We study whether neural quantum states based on multi-layer feed-forward networks can find ground states which exhibit volume-law entanglement entropy. As a testbed, we employ the paradigmatic Sachdev-Ye-Kitaev model. We find that both shallow and deep feed-forward networks require an exponential number of parameters in order to represent the ground state of this model. This demonstrates that sufficiently complicated quantum states, although being physical solutions to relevant models and not pathological cases, can still be difficult to learn to the point of intractability at larger system sizes. This highlights the importance of further investigations into the physical properties of quantum states amenable to an efficient neural representation.
