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Empowering deep neural quantum states through efficient optimization.

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  • 1Center for Electronic Correlations and Magnetism, University of Augsburg, Augsburg, Germany.

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A new optimization algorithm enables training deep neural quantum states for complex quantum systems. This breakthrough allows for precise computation of ground states and reveals evidence of quantum-spin-liquid phases.

Keywords:
Computational scienceElectronic properties and materials

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Area of Science:

  • Quantum physics
  • Computational physics
  • Artificial intelligence

Background:

  • Computing ground states of interacting quantum matter is challenging, particularly for 2D systems.
  • Neural quantum states offer a promising approach by using neural networks to represent wavefunctions.
  • Existing optimization algorithms struggle with large-scale deep neural network architectures.

Purpose of the Study:

  • To develop an optimization algorithm suitable for training deep neural quantum states.
  • To apply this method to complex frustrated spin models.
  • To investigate the potential for discovering new quantum phases.

Main Methods:

  • Introduction of a minimum-step stochastic-reconfiguration optimization algorithm.
  • Training deep neural quantum states with up to 10^6 parameters.
  • Application to frustrated spin-1/2 models on square and triangular lattices.

Main Results:

  • Trained deep networks achieved machine precision.
  • Improved variational energies were obtained compared to existing results.
  • Numerical evidence for gapless quantum-spin-liquid phases was found.

Conclusions:

  • The new optimization algorithm effectively trains large-scale deep neural quantum states.
  • The method accurately captures emergent complexity in quantum many-body problems.
  • This work provides evidence for elusive quantum-spin-liquid phases.