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Area of Science:

  • Quantum physics
  • Computational physics
  • Machine learning

Background:

  • Simulating open quantum many-body systems is computationally challenging.
  • Existing methods struggle with the complexity of mixed quantum states and dissipation.
  • Accurate simulations are crucial for understanding quantum phenomena.

Purpose of the Study:

  • To develop a novel variational approach for simulating open quantum many-body systems.
  • To utilize deep autoregressive neural networks for efficient state representation.
  • To dynamically adapt quantum state parameters using the Lindblad master equation.

Main Methods:

  • Employed a time-dependent variational principle.
  • Utilized deep autoregressive neural networks for compressed state representation.
  • Applied the Lindblad master equation for parameter evolution.
  • Tested on the dissipative quantum Heisenberg model in 1D and 2D.

Main Results:

  • Successfully simulated the dynamics of open quantum systems with dissipation.
  • Demonstrated scalability for systems up to 40 spins in 1D and 4x4 in 2D.
  • Applied the method to simulate confinement dynamics under dissipation.

Conclusions:

  • The developed variational approach with deep neural networks offers an efficient method for simulating open quantum many-body systems.
  • This technique provides a powerful tool for studying complex quantum dynamics, including dissipative processes.
  • The approach shows promise for future investigations in quantum physics and materials science.