提出新超参数调控量子张量网络中后选择程度，提升量子机器学习性能。
Entanglement is Half the Story: Post-Selection vs. Partial Traces
- 用后选择程度控制张量网络的量子约束强度，实现经典与量子模型统一框架。
- 引入可训练超参数，将有限后选择能力动态分配给量子模型，提升性能。
- 为量子机器学习提供可调制的混合架构，适合研究量子优势与模型优化。
尽管张量网络传统上用于模拟量子系统，近十年来也逐渐被视作机器学习模型。本文结合两领域经验，揭示张量网络上的量子约束如何影响其能力。通过在量子计算机上实现经典张量网络的推断方法，构建了一种混合架构，统一了经典与量子张量网络的边缘情况。我们识别出后选择是该插值的关键属性：后选择程度决定了量子约束的施加强度。基于此，提出一个新超参数，用于控制混合与量子张量网络之间的过渡。在经典与量子张量网络的对比中，该超参数可类比于键维数（bond dimension）。通过该超参数，可将实际受限的后选择能力以可训练方式分配给量子模型，从而改善量子机器学习性能。
原文摘要 · Abstract (English)
While tensor networks have their traditional application in simulating quantum systems, in the recent decade they have gathered interest as machine learning models. We combine the experience from both fields and derive how quantum constraints placed on a tensor network manifest a change in capabilities. To this end, we employ a method of inference of classical tensor networks on a quantum computer to define a hybrid architecture. This hybrid tensor network is a practical unified framework for it's classical and quantum tensor network edge cases. We identify post-selection as the important property on which this interpolation hinges. The amount of post-selection corresponds to the level to which quantum constraints are enforced on the tensor network. On this basis, we propose a new hyperparameter which controls the transition between the hybrid and the quantum tensor network. In the comparison of classical and quantum tensor networks it complements the bond dimension. Quantum machine learning is improved by using the hyperparameter to allocate the practically limited post-selection to the quantum model in a trainable manner.
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