Quantum computing is a growing field that promises many real-world
applications such as quantum cryptography or quantum finance. The number of
people able to use quantum computing is however still very small. This
limitation comes from the difficulty to understand the concepts and to know how
to start coding. Therefore, there is a need for tools that can assist
non-expert in overcoming this complexity. One possibility would be to use
existing conversational agents. Unfortunately ChatGPT and other Large-Language
Models produce inaccurate results. This article presents C4Q, a chatbot that
answers accurately basic questions and guides users when trying to code quantum
programs. Contrary to other approaches C4Q uses a pre-trained large language
model only to discover and classify user requests. It then generates an
accurate answer using an own engine. Thanks to this architectural design, C4Q's
answers are always correct, and thus C4Q can become a support tool that makes
quantum computing more available to non-experts.

C4Q 是一个聊天机器人，使用预训练的大型语言模型发现和分类用户请求，然后使用自己的引擎生成准确的答案，使量子计算更易于非专业人士使用。

C4Q：一个针对量子的聊天机器人

C4Q: A Chatbot for Quantum

Reading levels are highly individual and can depend on a text's language, a
person's cognitive abilities, or knowledge on a topic. Text simplification is
the task of rephrasing a text to better cater to the abilities of a specific
target reader group. Simplification of scientific abstracts helps non-experts
to access the core information by bypassing formulations that require domain or
expert knowledge. This is especially relevant for, e.g., cancer patients
reading about novel treatment options. The SimpleText lab hosts the
simplification of scientific abstracts for non-experts (Task 3) to advance this
field. We contribute three runs employing out-of-the-box summarization models
(two based on T5, one based on PEGASUS) and one run using ChatGPT with complex
phrase identification.

科学摘要简化是为非专家出版物提供核心信息的重要任务，包括使用简化模型和聊天 GPT 进行摘要。

科学文本简化用于非专业读者

Text Simplification of Scientific Texts for Non-Expert Readers

Quantifying image distortions caused by strong gravitational lensing and
estimating the corresponding matter distribution in lensing galaxies has been
primarily performed by maximum likelihood modeling of observations. This is
typically a time and resource-consuming procedure, requiring sophisticated
lensing codes, several data preparation steps, and finding the maximum
likelihood model parameters in a computationally expensive process with
downhill optimizers. Accurate analysis of a single lens can take up to a few
weeks and requires the attention of dedicated experts. Tens of thousands of new
lenses are expected to be discovered with the upcoming generation of ground and
space surveys, the analysis of which can be a challenging task. Here we report
the use of deep convolutional neural networks to accurately estimate lensing
parameters in an extremely fast and automated way, circumventing the
difficulties faced by maximum likelihood methods. We also show that lens
removal can be made fast and automated using Independent Component Analysis of
multi-filter imaging data. Our networks can recover the parameters of the
Singular Isothermal Ellipsoid density profile, commonly used to model strong
lensing systems, with an accuracy comparable to the uncertainties of
sophisticated models, but about ten million times faster: 100 systems in
approximately 1s on a single graphics processing unit. These networks can
provide a way for non-experts to obtain lensing parameter estimates for large
samples of data. Our results suggest that neural networks can be a powerful and
fast alternative to maximum likelihood procedures commonly used in
astrophysics, radically transforming the traditional methods of data reduction
and analysis.

本文报道了使用深度卷积神经网络在非常快速且自动化的方式下准确估计透镜参数的方法，不需要使用最大似然方法，而且可以提供一种非专家获取大量数据的透镜参数估计的方法。