A promising strategy to protect quantum information from noise-induced errors
is to encode it into the low-energy states of a topological quantum memory
device. However, readout errors from such memory under realistic settings is
less understood. We study the problem of decoding quantum information encoded
in the groundspaces of topological stabilizer Hamiltonians in the presence of
generic perturbations, such as quenched disorder. We first prove that the
standard stabilizer-based error correction and decoding schemes work adequately
well in such perturbed quantum codes by showing that the decoding error
diminishes exponentially in the distance of the underlying unperturbed code. We
then prove that Quantum Neural Network (QNN) decoders provide an almost
quadratic improvement on the readout error. Thus, we demonstrate provable
advantage of using QNNs for decoding realistic quantum error-correcting codes,
and our result enables the exploration of a wider range of non-stabilizer codes
in the near-term laboratory settings.

一种保护量子信息免受噪音诱导错误的有前途的策略是将其编码到拓扑量子存储设备的低能态中。在现实情况下，对于这种存储器的读出错误却不为人所熟知。我们研究在存在诸如淬灭性紊乱等通用扰动的情况下，解码编码在拓扑稳定子哈密顿量的基态中的量子信息的问题。首先，我们通过证明在这种扰动的量子码中，基于稳定子的纠错和解码方案能够适当地发挥作用，从而使得解码误差按照基础未扰动码的距离呈指数下降。然后，我们证明了量子神经网络（QNN）编码器可以几乎使读出错误有二次改善。因此，我们证明了在解码现实量子纠错码时使用 QNN 的有利，这一结果使得在近期实验室环境中可以探索更广泛的非稳定子码范围。

量子神经网络作为量子信息解码器的优势

Advantage of Quantum Neural Networks as Quantum Information Decoders

Compared to sentence-level systems, document-level neural machine translation
(NMT) models produce a more consistent output across a document and are able to
better resolve ambiguities within the input. There are many works on
document-level NMT, mostly focusing on modifying the model architecture or
training strategy to better accommodate the additional context-input. On the
other hand, in most works, the question on how to perform search with the
trained model is scarcely discussed, sometimes not mentioned at all. In this
work, we aim to answer the question how to best utilize a context-aware
translation model in decoding. We start with the most popular document-level
NMT approach and compare different decoding schemes, some from the literature
and others proposed by us. In the comparison, we are using both, standard
automatic metrics, as well as specific linguistic phenomena on three standard
document-level translation benchmarks. We find that most commonly used decoding
strategies perform similar to each other and that higher quality context
information has the potential to further improve the translation.

本文研究如何利用上下文感知的翻译模型在解码时最佳应用。通过对比使用标准自动指标和三个标准的文档级翻译基准测试上的特定语言现象，我们发现大多数常用的解码策略表现相似，并且更高质量的上下文信息有潜力进一步改善翻译效果。