Inflation：用于经典与量子因果兼容性的 Python 库
Inflation: a Python library for classical and quantum causal compatibility
AI总结：
本文介绍 Inflation，一个用于评估观测概率分布与因果解释兼容性的 Python 库，支持经典和量子范式下的因果兼容性求解与优化，设计模块化且易于扩展。
摘要：
我们介绍 Inflation，一个用于评估观测到的概率分布是否与因果解释兼容的 Python 库。这是理论科学和应用科学中的一个核心问题，近期在量子非局域性领域取得了显著进展，即膨胀技术的发展。Inflation 是一个可扩展的工具包，能够解决纯因果兼容性问题，并在经典和量子范式下对兼容关联集合的（松弛）进行优化。该库设计为模块化且即用型，同时保持对底层对象的轻松访问以进行自定义修改。
英文摘要：
We introduce Inflation, a Python library for assessing whether an observed probability distribution is compatible with a causal explanation. This is a central problem in both theoretical and applied sciences, which has recently witnessed significant advances from the area of quantum nonlocality, namely, in the development of inflation techniques. Inflation is an extensible toolkit that is capable of solving pure causal compatibility problems and optimization over (relaxations of) sets of compatible correlations in both the classical and quantum paradigms. The library is designed to be modular and with the ability of being ready-to-use, while keeping an easy access to low-level objects for custom modifications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
