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Quantum semantics of text perception.

Ilya A Surov1, E Semenenko1, A V Platonov1

  • 1ITMO University, St. Petersburg, Russia, 197101.

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This study introduces a quantum model for subjective text perception using binary distinctions. The quantum cognitive state captures semantic connections between words, enabling advanced machine intelligence.

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Area of Science:

  • Cognitive Science
  • Quantum Computing
  • Computational Linguistics

Background:

  • Traditional semantic models struggle to incorporate the subjective nature of human perception.
  • Existing approaches lack a unified framework for neurophysiological, linguistic, and psychological aspects of cognition.

Purpose of the Study:

  • To present a quantum model for subjective text perception.
  • To develop a method for quantifying semantic connectivity between words using quantum principles.
  • To extend machine intelligence capabilities by integrating subjective perception.

Main Methods:

  • A quantum model based on binary cognitive distinctions for natural language words.
  • Representation of perception results as quantum cognitive states in Hilbert space.
  • Utilizing a two-qubit state and entanglement to measure semantic connectivity.

Main Results:

  • The quantum model accounts for the subjective dimension of perception, where results are constrained but not fully predetermined.
  • An algorithm for detecting and measuring semantic connectivity between word pairs was developed and experimentally validated.
  • The model successfully unifies diverse descriptions of cognition within quantum theory.

Conclusions:

  • The developed quantum approach offers a novel framework for understanding subjective text perception.
  • This research bridges cognitive science, linguistics, and quantum mechanics for enhanced AI.
  • The findings pave the way for more sophisticated and human-like machine intelligence.