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Quantum Approach for Contextual Search, Retrieval, and Ranking of Classical Information.

Alexander P Alodjants1, Anna E Avdyushina2, Dmitriy V Tsarev1

  • 1Institute of Advanced Data Transfer Systems, ITMO University, 197101 St. Petersburg, Russia.

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Summary
This summary is machine-generated.

This study introduces a quantum-inspired document search method using quantum probability theory. It effectively captures user preferences and semantic relationships, outperforming traditional cosine similarity for better search engine development.

Keywords:
decision makinginformation retrievalmachine learningordering effectquantum cognition

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Area of Science:

  • Computer Science
  • Quantum Information Science

Background:

  • Quantum-inspired algorithms leverage quantum science principles for advanced software information technologies.
  • Traditional search methods often struggle with nuanced user preferences and semantic context.

Purpose of the Study:

  • To propose a novel quantum approach for document search, retrieval, and ranking.
  • To account for specific user preferences in diverse contexts using quantum probability theory.

Main Methods:

  • Utilizing a quantum probability theory within the hyperspace analog to language (HAL) framework.
  • Employing Hilbert space for vector specification of words and documents.
  • Applying a Bell-like test inspired by quantum physics for document analysis.

Main Results:

  • Demonstrated that "entanglement" in document search relates to word co-occurrence in incompatible query contexts.
  • Highlighted the significant impact of user preferences and word order in small HAL windows.
  • Showcased advantages over cosine similarity by focusing on contextual and semantic relationships.

Conclusions:

  • The proposed quantum approach offers a more nuanced understanding of document relevance.
  • This method enables the creation of advanced information search engines without requiring resource-intensive deep learning.
  • It provides a foundation for next-generation search technologies prioritizing semantic understanding.