用于经典信息的上下文搜索、检索和排序的量子方法。

IF 2.1 3区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY
Entropy Pub Date : 2024-10-13 DOI:10.3390/e26100862
Alexander P Alodjants, Anna E Avdyushina, Dmitriy V Tsarev, Igor A Bessmertny, Andrey Yu Khrennikov
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引用次数: 0

摘要

量子启发算法是现代软件信息技术的一个重要方向,它使用了量子科学的启发式方法和途径。这项工作基于量子物理学中著名的类贝尔检验(Bell-like test),提出了一种用于文档搜索、检索和排序的量子方法。我们在超空间类比语言(HAL)框架中提出了量子概率论,利用希尔伯特空间进行单词和文档向量规范。量子方法允许考虑不同语境下的特定用户偏好。为了验证所提出的算法,我们使用了由 OpenAI GPT-4 模型生成的旅行社合成广告文本文件数据集。我们发现,双词文档搜索和检索中的 "纠缠 "可以被识别为两个词在不相容的查询上下文中频繁出现。我们发现,在 HAL 窗口相对较小的情况下,用户偏好和查询中的单词排序起着重要作用。与余弦相似度指标的比较表明,我们的方法的主要优势在于基于用户强制要求的词与词之间的上下文和语义关系,而不仅仅是它们在文本中的表面出现。我们的文档检索和排序方法允许创建不需要资源密集型深度机器学习算法的新型信息搜索引擎。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantum Approach for Contextual Search, Retrieval, and Ranking of Classical Information.

Quantum-inspired algorithms represent an important direction in modern software information technologies that use heuristic methods and approaches of quantum science. This work presents a quantum approach for document search, retrieval, and ranking based on the Bell-like test, which is well-known in quantum physics. We propose quantum probability theory in the hyperspace analog to language (HAL) framework exploiting a Hilbert space for word and document vector specification. The quantum approach allows for accounting for specific user preferences in different contexts. To verify the algorithm proposed, we use a dataset of synthetic advertising text documents from travel agencies generated by the OpenAI GPT-4 model. We show that the "entanglement" in two-word document search and retrieval can be recognized as the frequent occurrence of two words in incompatible query contexts. We have found that the user preferences and word ordering in the query play a significant role in relatively small sizes of the HAL window. The comparison with the cosine similarity metrics demonstrates the key advantages of our approach based on the user-enforced contextual and semantic relationships between words and not just their superficial occurrence in texts. Our approach to retrieving and ranking documents allows for the creation of new information search engines that require no resource-intensive deep machine learning algorithms.

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来源期刊
Entropy
Entropy PHYSICS, MULTIDISCIPLINARY-
CiteScore
4.90
自引率
11.10%
发文量
1580
审稿时长
21.05 days
期刊介绍: Entropy (ISSN 1099-4300), an international and interdisciplinary journal of entropy and information studies, publishes reviews, regular research papers and short notes. Our aim is to encourage scientists to publish as much as possible their theoretical and experimental details. There is no restriction on the length of the papers. If there are computation and the experiment, the details must be provided so that the results can be reproduced.
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