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Latent Space Purification via Neural Density Operators.

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This study introduces a novel machine learning approach using restricted Boltzmann machines to accurately represent and purify quantum mixed states for experimental applications. The method achieves high-fidelity quantum state tomography, competitive with existing techniques.

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Area of Science:

  • Quantum Information Science
  • Machine Learning Applications
  • Quantum Computing

Background:

  • Machine learning (ML) is increasingly explored for quantum device design and validation.
  • Neural networks are investigated for representing quantum states, crucial for tasks like state tomography.
  • Encoding quantum mixed states is a key challenge for applying ML to real-world quantum experiments.

Purpose of the Study:

  • To develop a tractable ML representation for quantum mixed states.
  • To enable ML methods for quantum state tomography in experimental settings.
  • To purify mixed quantum states using a novel neural network architecture.

Main Methods:

  • Parametrization of a quantum density matrix using a restricted Boltzmann machine (RBM).
  • Utilizing auxiliary degrees of freedom in the RBM's latent space to purify mixed states.
  • Numerical implementation and application to quantum state tomography of entangled photons.

Main Results:

  • The RBM-based method successfully purifies quantum mixed states.
  • Achieved high fidelities in quantum state tomography, comparable to standard methods.
  • Demonstrated the potential of ML for analyzing experimental quantum states.

Conclusions:

  • Restricted Boltzmann machines offer a viable method for representing and purifying quantum mixed states.
  • This approach enhances the applicability of ML techniques in experimental quantum information science.
  • The developed method provides a competitive alternative for quantum state tomography.