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Large-scale stochastic simulation of open quantum systems.

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We developed the tensor jump method (TJM), a scalable algorithm for simulating open quantum systems. This method efficiently handles complex quantum dynamics, paving the way for more stable quantum technologies.

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Area of Science:

  • Quantum Physics
  • Computational Physics

Background:

  • Simulating open quantum systems with non-unitary dynamics is computationally challenging.
  • Understanding quantum-environment interactions is vital for quantum technologies and physical models.

Purpose of the Study:

  • Introduce a scalable and parallelizable algorithm for simulating large-scale open quantum systems.
  • Overcome the computational demands of simulating non-unitary quantum dynamics.

Main Methods:

  • Developed the tensor jump method (TJM), extending Monte Carlo wave function (MCWF) to matrix product states.
  • Employed dynamic time-dependent variational principle (TDVP) to minimize errors.
  • Introduced a sampling MPS to reduce timestep dependence.

Main Results:

  • The TJM demonstrates efficient scaling and convergence to Lindbladian dynamics, independent of system size.
  • Successfully simulated XXX Heisenberg models with up to 1000 spins on a CPU.
  • Rigorous and numerical validation of the method's accuracy and scalability.

Conclusions:

  • The TJM is a significant advancement for simulating open quantum systems.
  • Enables exploration of dissipative many-body dynamics.
  • Facilitates the design of more stable quantum hardware.