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Classical Data in Quantum Machine Learning Algorithms: Amplitude Encoding and the Relation Between Entropy and

Jurek Eisinger1, Ward Gauderis1, Lin de Huybrecht1

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Entropy (Basel, Switzerland)
|April 26, 2025
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Summary

The Categorical Compositional Distributional (DisCoCat) model uses quantum states for word meanings. Amplitude encoding classical data enhances quantum natural language processing models and clarifies the link between entropy and sentence ambiguity.

Keywords:
quantum machine learningquantum natural language processingsyntactic ambiguity

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Area of Science:

  • Quantum Computing
  • Computational Linguistics
  • Natural Language Processing

Background:

  • The Categorical Compositional Distributional (DisCoCat) model represents word meanings as quantum states, interacting via grammar.
  • This model has been extended using density matrices to address linguistic ambiguity.
  • Measures like von Neumann entropy and fidelity quantify the mixedness of these density matrices.

Purpose of the Study:

  • To investigate the impact of amplitude encoding classical data in quantum machine learning for natural language processing.
  • To explore how amplitude-encoded data affects the relationship between density matrix mixedness (entropy) and linguistic ambiguity.

Main Methods:

  • Utilizing amplitude encoding to introduce classical data into quantum machine learning algorithms.
  • Applying von Neumann entropy and fidelity as measures of mixedness in density matrices representing sentences.
  • Analyzing the interpretability of the entropy-ambiguity relationship with encoded data.

Main Results:

  • Amplitude encoding of data can improve the performance of quantum machine learning models in quantum natural language processing.
  • The study provides insights into how encoded classical data influences the connection between entropy and ambiguity.
  • The interpretability of the entropy-ambiguity relationship is significantly enhanced by amplitude encoding.

Conclusions:

  • Amplitude encoding is a valuable technique for improving quantum natural language processing models.
  • Classical data encoding makes the relationship between density matrix entropy and sentence ambiguity more intuitively understandable.
  • This research bridges quantum mechanics, linguistics, and machine learning for enhanced language understanding.