Modeling quantum entanglements in quantum language models

Mengjiao Xie, Yuexian Hou, Peng Zhang, Jingfei Li, Wenjie Li, Dawei Song

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

13 Citations (Scopus)


Recently, a Quantum Language Model (QLM) was proposed to model term dependencies upon Quantum Theory (QT) framework and successively applied in Information Retrieval (IR). Nevertheless, QLM's dependency is based on co-occurrences of terms and has not yet taken into account the Quantum Entanglement (QE), which is a key quantum concept and has a significant cognitive implication. In QT, an entangled state can provide a more complete description for the nature of realities, and determine intrinsic correlations of considered objects globally, rather than those co-occurrences on the surface. It is, however, a real challenge to decide and measure QE using the classical statistics of texts in a post-measurement configuration. In order to circumvent this problem, we theoretically prove the connection between QE and statistically Unconditional Pure Dependence (UPD). Since UPD has an implementable deciding algorithm, we can in turn characterize QE by extracting the UPD patterns from texts. This leads to a measurable QE, based on which we further advance the existing QLM framework. We empirically compare our model with related models, and the results demonstrate the effectiveness of our model.
Original languageEnglish
Title of host publicationIJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence
PublisherInternational Joint Conferences on Artificial Intelligence
Number of pages7
ISBN (Electronic)9781577357384
Publication statusPublished - 1 Jan 2015
Event24th International Joint Conference on Artificial Intelligence, IJCAI 2015 - Buenos Aires, Argentina
Duration: 25 Jul 201531 Jul 2015


Conference24th International Joint Conference on Artificial Intelligence, IJCAI 2015
CityBuenos Aires

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this