In quantum information and computation, entanglement is a resource. When combining concepts, the application of entanglement outside of micro-physical systems is an useful tool. We suggest new cognitive image-based tests that do not need to be translated. No prior knowledge of terms related to the concepts is required, therefore the choice is more intuitive. We examine the merging of two concepts that establish non-classical statistical correlation and present an entanglement-aware vector encoding algorithm. This research’s added value results in an automated system that teaches artificial intelligence to identify and handle entangled concepts.

中文翻译：

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扩展模型语言来处理人工智能中的纠缠概念

在量子信息和计算中，纠缠是一种资源。当结合概念时，在微观物理系统之外应用纠缠是一个有用的工具。我们建议不需要翻译的新的基于认知图像的测试。不需要先了解与概念相关的术语，因此选择更加直观。我们研究了建立非经典统计相关性的两个概念的合并，并提出了一种纠缠感知向量编码算法。这项研究的附加值产生了一个自动化系统，该系统教会人工智能识别和处理相互纠缠的概念。