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Tensor-Network Simulations of the Surface Code under Realistic Noise.

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Area of Science:

  • Quantum computing
  • Quantum error correction
  • Computational physics

Background:

  • The surface code is a leading quantum error correction code.
  • Simulating its performance under realistic noise is computationally challenging.
  • Existing threshold calculations often rely on simplified noise models.

Purpose of the Study:

  • To develop a tensor-network algorithm for simulating surface code error correction with arbitrary local noise.
  • To accurately determine the error threshold and subthreshold behavior of the surface code.
  • To compare simulation results with and without standard noise model approximations.

Main Methods:

  • Developed a novel tensor-network algorithm tailored for simulating quantum error correction.
  • Applied the algorithm to analyze the surface code under amplitude damping and systematic rotation noise channels.
  • Performed numerical simulations to evaluate the code's threshold and performance.

Main Results:

  • The tensor-network algorithm enables simulation of surface code with arbitrary local noise.
  • The study reveals the threshold and subthreshold dynamics for specific noise channels.
  • Results highlight discrepancies between simulations with realistic noise and those using approximated noise models.

Conclusions:

  • The developed algorithm provides a more accurate method for assessing surface code performance.
  • Realistic noise models are crucial for understanding the practical limitations of quantum error correction.
  • Further research can leverage this method to explore other noise types and code parameters.