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Related Experiment Video

Updated: Sep 13, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

A Quantum-like Approach to Semantic Text Classification.

Anastasia S Gruzdeva¹, Rodion N Iurev², Igor A Bessmertny²

¹National Center for Cognitive Research, National Research University for Information Technology, Mechanics and Optics (ITMO), St. Petersburg 197101, Russia.

Entropy (Basel, Switzerland)

|July 29, 2025

Summary

This summary is machine-generated.

This study introduces a quantum-like wave model for sentiment analysis, improving text classification accuracy by 15% over classical methods. This approach offers a computationally efficient alternative to machine learning (ML) for analyzing text data.

Keywords:

interference quantum-like heuristic algorithms sentiment analysis text classification vector-space language model

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Area of Science:

Natural Language Processing
Computational Linguistics
Quantum-Inspired Computing

Background:

Traditional machine learning (ML) methods face challenges in text classification and analysis.
Existing models often overlook complex semantic relationships within language.
A need exists for alternative text representation models that capture nuanced linguistic structures.

Purpose of the Study:

To explore a quantum-like (wave-based) model as an alternative to ML for sentiment analysis.
To investigate the impact of semantic interference on text classification accuracy.
To develop computationally efficient algorithms for wave-based text representation.

Main Methods:

Sentiment analysis of English-language reviews using a quantum-like wave model.
Exploration of text segmentation algorithms influenced by language structure.
Comparison of quantum-like model results with classical probabilistic methods.
Development of optimization techniques to reduce computational complexity.

Main Results:

The quantum-like model improved classification accuracy by approximately 15% compared to classical methods.
The model achieved precision and recall scores around 0.8 for classification tasks.
A proposed optimization reduced the algorithm's computational complexity from O(n^2) to O(n).

Conclusions:

The quantum-like wave model is a viable alternative or complement to traditional ML approaches for text analysis.
Accounting for quantum-like semantic interference enhances classification accuracy.
The developed model offers significant computational efficiency improvements.