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Toward Transparent and Controllable Quantum Generative Models.

Jinkai Tian1, Wenjing Yang2

  • 1Intelligent Game and Decision Lab, Beijing 100071, China.

Entropy (Basel, Switzerland)
|November 27, 2024
PubMed
Summary
This summary is machine-generated.

We introduce model inversion to make quantum generative models more understandable and controllable. This method traces quantum states back to their inputs, improving applications in quantum chemistry and materials science.

Keywords:
autoencoderexplainable artificial intelligencequantum neural networks

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Area of Science:

  • Quantum computing
  • Artificial intelligence

Background:

  • Quantum generative models show potential in quantum chemistry, materials science, and optimization.
  • A key limitation is their lack of interpretability, hindering practical application.

Purpose of the Study:

  • To introduce model inversion for enhanced interpretability and controllability of quantum generative models.
  • To establish a link between latent variables and generated quantum states.

Main Methods:

  • Developed and applied the model inversion technique to quantum generative models.
  • Utilized model inversion to analyze models generating ground states for Hamiltonians, including the transverse-field Ising model (TFIM).

Main Results:

  • Achieved interpretability control without the need for model retraining.
  • Demonstrated accurate guidance of generated quantum states across different quantum phases.
  • Successfully traced generated quantum states back to their latent variables.

Conclusions:

  • Model inversion significantly enhances the transparency and fine-tuning capabilities of quantum generative models.
  • This framework bridges the gap between theoretical quantum models and practical applications in fields like drug discovery and material design.