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Area of Science:

  • Quantum computing
  • Condensed matter physics
  • Machine learning

Background:

  • Increasing complexity of quantum systems necessitates automated tuning.
  • Quantum bit arrays and Majorana wires present significant tuning challenges.
  • Disorder effects can destroy crucial quantum phenomena.

Purpose of the Study:

  • Investigate machine learning for automated tuning of quantum gate arrays.
  • Apply the covariance matrix adaptation evolution strategy (CMA-ES) to Majorana wires.
  • Assess the algorithm's ability to improve topological signatures and mitigate disorder.

Main Methods:

  • Utilized machine learning, specifically CMA-ES, for tuning quantum gate arrays.
  • Focused on Majorana wires as a case study with strong intrinsic disorder.
  • Optimized gate voltages to recover quantum properties.

Main Results:

  • The CMA-ES algorithm efficiently improved topological signatures.
  • The algorithm successfully learned intrinsic disorder profiles within the quantum system.
  • Complete elimination of disorder effects was achieved, recovering Majorana zero modes.
  • Full recovery of Majorana zero modes was possible with only 20 optimized gates.

Conclusions:

  • Machine learning-based tuning is a viable and efficient approach for complex quantum systems.
  • CMA-ES can effectively overcome disorder in quantum systems like Majorana wires.
  • Automated tuning strategies are crucial for advancing quantum computing hardware.