Many inference scenarios rely on extracting relevant information from known
data in order to make future predictions. When the underlying stochastic
process satisfies certain assumptions, there is a direct mapping between its
exact classical and quantum simulators, with the latter asymptotically using
less memory. Here we focus on studying whether such quantum advantage persists
when those assumptions are not satisfied, and the model is doomed to have
imperfect accuracy. By studying the trade-off between accuracy and memory
requirements, we show that quantum models can reach the same accuracy with less
memory, or alternatively, better accuracy with the same memory. Finally, we
discuss the implications of this result for learning tasks.

在研究中，我们探讨了经典和量子模拟器之间的直接映射，以及量子模型在准确性和存储需求之间的权衡，证明了量子模型可以以更小内存达到相同准确性，或者以相同内存实现更好的准确性，并讨论了该结果对学习任务的影响。

量子模拟随机过程中的准确性与内存优势

Accuracy vs Memory Advantage in the Quantum Simulation of Stochastic  Processes

Additive interventions are a recently-proposed mechanism for controlling
target-side attributes in neural machine translation. In contrast to tag-based
approaches which manipulate the raw source sequence, interventions work by
directly modulating the encoder representation of all tokens in the sequence.
We examine the role of additive interventions in a large-scale multi-domain
machine translation setting and compare its performance in various inference
scenarios. We find that while the performance difference is small between
intervention-based systems and tag-based systems when the domain label matches
the test domain, intervention-based systems are robust to label error, making
them an attractive choice under label uncertainty. Further, we find that the
superiority of single-domain fine-tuning comes under question when training
data size is scaled, contradicting previous findings.

本文探讨加性干预在大规模多领域机器翻译中的作用以及与标签基础方法在各推断阶段的性能差异，发现加性干预在标签不确定性条件下更具有鲁棒性，且单领域微调的优势在数据规模扩大时存在疑问。